fl compression

mindspore/ccsrc/fl/CMakeLists.txt

@@ -51,6 +51,8 @@ if(NOT ENABLE_CPU OR WIN32)
     list(REMOVE_ITEM _FL_SRC_FILES "armour/cipher/cipher_reconstruct.cc")
     list(REMOVE_ITEM _FL_SRC_FILES "armour/cipher/cipher_shares.cc")
     list(REMOVE_ITEM _FL_SRC_FILES "armour/cipher/cipher_unmask.cc")
+    list(REMOVE_ITEM _FL_SRC_FILES "compression/decode_executor.cc")
+    list(REMOVE_ITEM _FL_SRC_FILES "compression/encode_executor.cc")
 endif()
 
 if(CMAKE_SYSTEM_NAME MATCHES "Darwin")

mindspore/ccsrc/fl/compression/decode_executor.cc

@@ -0,0 +1,150 @@
+/**
+ * Copyright 2022 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "fl/compression/decode_executor.h"
+
+namespace mindspore {
+namespace fl {
+namespace compression {
+std::vector<int> DecodeExecutor::ConstructMaskArray(int seed, float upload_sparse_rate, size_t param_num) {
+  static int multiplier = 2147483647;
+  static double increment = 4294967294.0;
+  static int modulo = 48271;
+  size_t retain_num = size_t(static_cast<float>(param_num) * upload_sparse_rate);
+  if (retain_num == 0) {
+    MS_LOG(WARNING) << "The retain_num is 0, and upload_sparse_rate is too small.";
+  }
+  std::vector<int> mask_array(param_num, 0);
+  for (size_t i = 0; i < retain_num; ++i) {
+    mask_array[i] = 1;
+  }
+
+  seed = ((seed + multiplier) * modulo) % multiplier;
+  for (size_t i = 0; i < param_num; ++i) {
+    // generate random number in (0, 1)
+    double rand = static_cast<double>(seed) / increment + 0.5;
+    // update seed
+    seed = (seed * modulo) % multiplier;
+    size_t j = size_t(rand * static_cast<double>(param_num - i)) + i;
+    int temp = mask_array[i];
+    mask_array[i] = mask_array[j];
+    mask_array[j] = temp;
+  }
+  return mask_array;
+}
+
+bool DecodeExecutor::DeQuantSparseDiff(std::map<std::string, std::vector<float>> *weight_map,
+                                       const std::vector<CompressFeatureMap> &compress_feature_maps, size_t num_bits,
+                                       float upload_sparse_rate, int seed, const std::vector<std::string> &name_vec,
+                                       size_t data_size) {
+  std::vector<std::vector<float>> decompress_feature_maps;
+
+  // origin parameters
+  std::vector<size_t> shape_vec;
+  size_t param_num = 0;
+  const auto &iter_to_model = mindspore::fl::server::ModelStore::GetInstance().iteration_to_model();
+  size_t latest_iter_num = iter_to_model.rbegin()->first;
+  std::map<std::string, AddressPtr> feature_maps =
+    mindspore::fl::server::ModelStore::GetInstance().GetModelByIterNum(latest_iter_num);
+  // get shape vector and number of upload parameters
+  for (const auto &name : name_vec) {
+    size_t shape = feature_maps[name]->size / sizeof(float);
+    shape_vec.emplace_back(shape);
+    param_num += shape;
+  }
+  MS_LOG(DEBUG) << "Compression get last weights success!";
+
+  // quant decode
+  auto temp1 = static_cast<float>(1 << num_bits) - 1.0f;
+  auto temp2 = static_cast<float>(1 << (num_bits - 1));
+  std::vector<float> de_min_max_feature_map;
+  for (auto compress_feature_map : compress_feature_maps) {
+    float min_val = compress_feature_map.min_val;
+    float max_val = compress_feature_map.max_val;
+    float scale_val = static_cast<float>(max_val - min_val) / temp1 + 1e-10f;
+    size_t size = compress_feature_map.compress_data.size();
+    for (size_t i = 0; i < size; ++i) {
+      de_min_max_feature_map.emplace_back(
+        (static_cast<float>(compress_feature_map.compress_data[i]) + temp2) * scale_val + min_val);
+    }
+  }
+  MS_LOG(DEBUG) << "Compression quant decode success!";
+
+  // sparse decode
+  std::vector<int> mask_array = ConstructMaskArray(seed, upload_sparse_rate, param_num);
+  size_t index = 0;
+  size_t de_min_max_feature_map_index = 0;
+  for (const auto &shape : shape_vec) {
+    std::vector<float> feature_map(shape);
+    for (size_t i = 0; i < shape; ++i) {
+      if (index >= mask_array.size()) {
+        MS_LOG(WARNING) << "The mask_array and parameter shape is not matched.";
+        return false;
+      }
+      if (mask_array[index] == 1) {
+        if (de_min_max_feature_map_index >= de_min_max_feature_map.size()) {
+          MS_LOG(WARNING) << "The number of upload parameters is too small.";
+          return false;
+        }
+        feature_map[i] = de_min_max_feature_map[de_min_max_feature_map_index];
+        de_min_max_feature_map_index += 1;
+      } else {
+        feature_map[i] = 0.0f;
+      }
+      index += 1;
+    }
+    decompress_feature_maps.emplace_back(feature_map);
+  }
+  MS_LOG(DEBUG) << "Compression sparse decode success!";
+
+  // difference decode
+  for (size_t i = 0; i < decompress_feature_maps.size(); ++i) {
+    size_t feature_size = decompress_feature_maps[i].size();
+    std::string name = name_vec[i];
+    float *weight_data = reinterpret_cast<float *>(feature_maps[name]->addr);
+    auto &weight_item = (*weight_map)[name];
+    weight_item.resize(feature_size);
+    for (size_t j = 0; j < feature_size; ++j) {
+      weight_item[j] = decompress_feature_maps[i][j] + data_size * weight_data[j];
+    }
+  }
+  MS_LOG(DEBUG) << "Compression difference decode success!";
+
+  return true;
+}
+
+bool DecodeExecutor::Decode(std::map<std::string, std::vector<float>> *weight_map,
+                            const std::vector<CompressFeatureMap> &compress_feature_maps,
+                            schema::CompressType upload_compress_type, float upload_sparse_rate, int seed,
+                            const std::vector<std::string> &name_vec, size_t data_size) {
+  if (upload_compress_type == schema::CompressType_DIFF_SPARSE_QUANT) {
+    return DeQuantSparseDiff(weight_map, compress_feature_maps, 8, upload_sparse_rate, seed, name_vec, data_size);
+  }
+  return false;
+}
+
+schema::CompressType DecodeExecutor::GetCompressType(schema::CompressType upload_compress_type) {
+  if (upload_compress_type == schema::CompressType_DIFF_SPARSE_QUANT) {
+    MS_LOG(DEBUG) << "This upload compress type is DIFF_SPARSE_QUANT.";
+    return schema::CompressType_DIFF_SPARSE_QUANT;
+  }
+
+  MS_LOG(DEBUG) << "This upload compress type is NO_COMPRESS.";
+  return schema::CompressType_NO_COMPRESS;
+}
+}  // namespace compression
+}  // namespace fl
+}  // namespace mindspore

mindspore/ccsrc/fl/compression/decode_executor.h

@@ -0,0 +1,74 @@
+/**
+ * Copyright 2022 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_FL_COMPRESSION_DECODE_EXECUTOR_H_
+#define MINDSPORE_CCSRC_FL_COMPRESSION_DECODE_EXECUTOR_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <functional>
+#include <algorithm>
+#include <regex>
+#include <map>
+#include <utility>
+#include "proto/comm.pb.h"
+#include "schema/fl_job_generated.h"
+#include "schema/cipher_generated.h"
+#include "fl/server/model_store.h"
+#include "fl/server/common.h"
+#include "ps/ps_context.h"
+
+namespace mindspore {
+namespace fl {
+namespace compression {
+struct CompressFeatureMap {
+  std::string weight_fullname;
+  std::vector<int8_t> compress_data;
+  float min_val;
+  float max_val;
+};
+
+class DecodeExecutor {
+ public:
+  static DecodeExecutor &GetInstance() {
+    static DecodeExecutor instance;
+    return instance;
+  }
+
+  // construct mask array for random sparse
+  std::vector<int> ConstructMaskArray(int seed, float upload_sparse_rate, size_t param_num);
+
+  // decode min_max quantization and random sparse and parameter difference
+  bool DeQuantSparseDiff(std::map<std::string, std::vector<float>> *weight_map,
+                         const std::vector<CompressFeatureMap> &compress_feature_maps, size_t num_bits,
+                         float upload_sparse_rate, int seed, const std::vector<std::string> &name_vec,
+                         size_t data_size);
+
+  // decode
+  bool Decode(std::map<std::string, std::vector<float>> *weight_map,
+              const std::vector<CompressFeatureMap> &compress_feature_maps, schema::CompressType upload_compress_type,
+              float upload_sparse_rate, int seed, const std::vector<std::string> &name_vec, size_t data_size);
+
+  schema::CompressType GetCompressType(schema::CompressType upload_compress_type);
+};
+}  // namespace compression
+}  // namespace fl
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_FL_COMPRESSION_DECODE_EXECUTOR_H_

mindspore/ccsrc/fl/compression/encode_executor.cc

@@ -0,0 +1,102 @@
+/**
+ * Copyright 2022 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "fl/compression/encode_executor.h"
+
+#include <arpa/inet.h>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <functional>
+#include <algorithm>
+#include <regex>
+#include <map>
+#include <utility>
+#include <vector>
+#include "fl/server/common.h"
+
+namespace mindspore {
+namespace fl {
+namespace compression {
+bool CompressExecutor::EnableCompressWeight(const schema::CompressType compressType) {
+  return kCompressTypeMap.count(compressType) > 0;
+}
+
+bool CompressExecutor::construct_compress_weight(std::map<std::string, CompressWeight> *compressWeights,
+                                                 std::map<std::string, std::vector<float>> feature_maps,
+                                                 const schema::CompressType compressType) {
+  if (compressType == schema::CompressType_QUANT) {
+    return quant_min_max(compressWeights, feature_maps, kCompressTypeMap.at(compressType));
+  }
+  return false;
+}
+
+bool CompressExecutor::quant_min_max(std::map<std::string, CompressWeight> *compressWeights,
+                                     std::map<std::string, std::vector<float>> feature_maps, size_t num_bits) {
+  auto temp1 = static_cast<float>(1 << num_bits) - 1.0f;
+  auto temp2 = static_cast<float>(1 << (num_bits - 1));
+  for (const auto &feature_map : feature_maps) {
+    std::string weight_name = feature_map.first;
+    float min_value = 1e10f;
+    float max_value = -min_value;
+    for (const auto &feature : feature_map.second) {
+      if (feature > max_value) {
+        max_value = feature;
+      }
+      if (feature < min_value) {
+        min_value = feature;
+      }
+    }
+    float scale_value = (max_value - min_value) / temp1 + 1e-10f;
+    size_t size = feature_map.second.size();
+    if (size == 0) {
+      MS_LOG(WARNING) << "The size of parameters is zero.";
+      return false;
+    }
+    CompressWeight compressWeight;
+    for (size_t i = 0; i < size; ++i) {
+      auto round_data = round((feature_map.second[i] - min_value) / scale_value - temp2);
+      // bit pack can be implemented here in the future
+      auto int8_data = int8_t(round_data);
+      compressWeight.compress_data.emplace_back(int8_data);
+    }
+    compressWeight.min_val = min_value;
+    compressWeight.max_val = max_value;
+    compressWeight.compress_data_len = size;
+
+    (*compressWeights)[weight_name] = compressWeight;
+  }
+  return true;
+}
+
+schema::CompressType CompressExecutor::GetCompressType(const flatbuffers::Vector<int8_t> *download_compress_types) {
+  schema::CompressType compressType = schema::CompressType_NO_COMPRESS;
+  if (download_compress_types == nullptr) {
+    MS_LOG(DEBUG) << "The client does not support current download compress type.";
+  } else {
+    for (size_t i = 0; i < download_compress_types->size(); ++i) {
+      auto download_compress_type = download_compress_types->Get(i);
+      if (download_compress_type == schema::CompressType_QUANT) {
+        compressType = schema::CompressType_QUANT;
+        break;
+      }
+    }
+  }
+  return compressType;
+}
+}  // namespace compression
+}  // namespace fl
+}  // namespace mindspore

mindspore/ccsrc/fl/compression/encode_executor.h

@@ -0,0 +1,68 @@
+/**
+ * Copyright 2022 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_FL_COMPRESSION_ENCODE_EXECUTOR_H_
+#define MINDSPORE_CCSRC_FL_COMPRESSION_ENCODE_EXECUTOR_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+#include <map>
+#include "proto/comm.pb.h"
+#include "schema/fl_job_generated.h"
+#include "schema/cipher_generated.h"
+#include "fl/armour/secure_protocol/key_agreement.h"
+#include "ps/ps_context.h"
+#include "ps/core/worker_node.h"
+#include "ps/core/cluster_metadata.h"
+#include "ps/core/communicator/tcp_communicator.h"
+#include "fl/server/common.h"
+
+namespace mindspore {
+namespace fl {
+namespace compression {
+// compress type map: schema::CompressType -> num bits
+const std::map<schema::CompressType, size_t> kCompressTypeMap = {{schema::CompressType_QUANT, 8}};
+
+struct CompressWeight {
+  std::vector<int8_t> compress_data;
+  size_t compress_data_len;
+  float min_val;
+  float max_val;
+};
+
+class CompressExecutor {
+ public:
+  static CompressExecutor &GetInstance() {
+    static CompressExecutor instance;
+    return instance;
+  }
+
+  bool EnableCompressWeight(const schema::CompressType compressType);
+
+  bool construct_compress_weight(std::map<std::string, CompressWeight> *compressWeights,
+                                 std::map<std::string, std::vector<float>> feature_maps,
+                                 const schema::CompressType compressType);
+
+  bool quant_min_max(std::map<std::string, CompressWeight> *compressWeights,
+                     std::map<std::string, std::vector<float>> feature_maps, size_t num_bits);
+
+  schema::CompressType GetCompressType(const flatbuffers::Vector<int8_t> *download_compress_types);
+};
+}  // namespace compression
+}  // namespace fl
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_FL_COMPRESSION_ENCODE_EXECUTOR_H_

mindspore/ccsrc/fl/server/common.h

@@ -149,6 +149,11 @@ constexpr auto kUpdateModelRejectClientNum = "updateModelRejectClientNum";
 constexpr auto kGetModelTotalClientNum = "getModelTotalClientNum";
 constexpr auto kGetModelAcceptClientNum = "getModelAcceptClientNum";
 constexpr auto kGetModelRejectClientNum = "getModelRejectClientNum";
+constexpr auto kMinVal = "min_val";
+constexpr auto kMaxVal = "max_val";
+constexpr auto kQuant = "QUANT";
+constexpr auto kDiffSparseQuant = "DIFF_SPARSE_QUANT";
+constexpr auto kNoCompress = "NO_COMPRESS";
 
 // OptimParamNameToIndex represents every inputs/workspace/outputs parameter's offset when an optimizer kernel is
 // launched.

mindspore/ccsrc/fl/server/iteration.cc

@@ -588,6 +588,7 @@ void Iteration::Next(bool is_iteration_valid, const std::string &reason) {
 
     if (LocalMetaStore::GetInstance().verifyAggregationFeatureMap(feature_map)) {
       ModelStore::GetInstance().StoreModelByIterNum(iteration_num_, model);
+      ModelStore::GetInstance().StoreCompressModelByIterNum(iteration_num_, model);
       iteration_result_ = IterationResult::kSuccess;
       MS_LOG(INFO) << "Iteration " << iteration_num_ << " is successfully finished.";
     } else {
@@ -599,6 +600,7 @@ void Iteration::Next(bool is_iteration_valid, const std::string &reason) {
     size_t latest_iter_num = iter_to_model.rbegin()->first;
     const auto &model = ModelStore::GetInstance().GetModelByIterNum(latest_iter_num);
     ModelStore::GetInstance().StoreModelByIterNum(iteration_num_, model);
+    ModelStore::GetInstance().StoreCompressModelByIterNum(iteration_num_, model);
     iteration_result_ = IterationResult::kFail;
     MS_LOG(WARNING) << "Iteration " << iteration_num_ << " is invalid. Reason: " << reason;
   }

mindspore/ccsrc/fl/server/kernel/round/get_model_kernel.cc

@@ -92,7 +92,7 @@ void GetModelKernel::GetModel(const schema::RequestGetModel *get_model_req,
     return;
   }
   auto next_req_time = LocalMetaStore::GetInstance().value<uint64_t>(kCtxIterationNextRequestTimestamp);
-  std::map<std::string, AddressPtr> feature_maps;
+  std::map<std::string, AddressPtr> feature_maps = {};
   size_t current_iter = LocalMetaStore::GetInstance().curr_iter_num();
   size_t get_model_iter = IntToSize(get_model_req->iteration());
   const auto &iter_to_model = ModelStore::GetInstance().iteration_to_model();
@@ -110,6 +110,7 @@ void GetModelKernel::GetModel(const schema::RequestGetModel *get_model_req,
     SendResponseMsg(message, fbb->GetBufferPointer(), fbb->GetSize());
     return;
   }
+
   IncreaseAcceptClientNum();
   auto real_get_model_iter = get_model_iter;
   if (iter_to_model.count(get_model_iter) == 0) {
@@ -118,12 +119,37 @@ void GetModelKernel::GetModel(const schema::RequestGetModel *get_model_req,
                   << " is invalid. Current iteration is " << std::to_string(current_iter);
     real_get_model_iter = latest_iter_num;
   }
-  auto cache = ModelStore::GetInstance().GetModelResponseCache(name_, current_iter, real_get_model_iter);
+  auto download_compress_types = get_model_req->download_compress_types();
+  schema::CompressType compressType =
+    mindspore::fl::compression::CompressExecutor::GetInstance().GetCompressType(download_compress_types);
+  std::string compress_type;
+  if (compressType == schema::CompressType_QUANT) {
+    compress_type = kQuant;
+  } else {
+    compress_type = kNoCompress;
+  }
+  auto cache = ModelStore::GetInstance().GetModelResponseCache(name_, current_iter, real_get_model_iter, compress_type);
   if (cache == nullptr) {
-    feature_maps = ModelStore::GetInstance().GetModelByIterNum(real_get_model_iter);
+    // Only download compress weights if client support.
+    std::map<std::string, AddressPtr> compress_feature_maps = {};
+    if (compressType == schema::CompressType_NO_COMPRESS) {
+      feature_maps = ModelStore::GetInstance().GetModelByIterNum(real_get_model_iter);
+    } else {
+      auto compressExecutor = mindspore::fl::compression::CompressExecutor::GetInstance();
+      if (compressExecutor.EnableCompressWeight(compressType)) {
+        const auto &iter_to_compress_model = ModelStore::GetInstance().iteration_to_compress_model();
+        if (iter_to_compress_model.count(get_model_iter) == 0) {
+          MS_LOG(DEBUG) << "The iteration of GetCompressModel request " << std::to_string(get_model_iter)
+                        << " is invalid. Current iteration is " << std::to_string(current_iter);
+          compress_feature_maps = ModelStore::GetInstance().GetCompressModelByIterNum(latest_iter_num, compressType);
+        } else {
+          compress_feature_maps = ModelStore::GetInstance().GetCompressModelByIterNum(get_model_iter, compressType);
+        }
+      }
+    }
     BuildGetModelRsp(fbb, schema::ResponseCode_SUCCEED, "Get model for iteration " + std::to_string(get_model_iter),
-                     current_iter, feature_maps, std::to_string(next_req_time));
-    cache = ModelStore::GetInstance().StoreModelResponseCache(name_, current_iter, real_get_model_iter,
+                     current_iter, feature_maps, std::to_string(next_req_time), compressType, compress_feature_maps);
+    cache = ModelStore::GetInstance().StoreModelResponseCache(name_, current_iter, real_get_model_iter, compress_type,
                                                               fbb->GetBufferPointer(), fbb->GetSize());
     if (cache == nullptr) {
       SendResponseMsg(message, fbb->GetBufferPointer(), fbb->GetSize());
@@ -131,7 +157,7 @@ void GetModelKernel::GetModel(const schema::RequestGetModel *get_model_req,
     }
   }
   SendResponseMsgInference(message, cache->data(), cache->size(), ModelStore::GetInstance().RelModelResponseCache);
-  MS_LOG(DEBUG) << "GetModel last iteratin is valid or not: " << Iteration::GetInstance().is_last_iteration_valid()
+  MS_LOG(DEBUG) << "GetModel last iteration is valid or not: " << Iteration::GetInstance().is_last_iteration_valid()
                 << ", next request time is " << next_req_time << ", current iteration is " << current_iter;
   return;
 }
@@ -139,7 +165,8 @@ void GetModelKernel::GetModel(const schema::RequestGetModel *get_model_req,
 void GetModelKernel::BuildGetModelRsp(const std::shared_ptr<FBBuilder> &fbb, const schema::ResponseCode retcode,
                                       const std::string &reason, const size_t iter,
                                       const std::map<std::string, AddressPtr> &feature_maps,
-                                      const std::string &timestamp) {
+                                      const std::string &timestamp, const schema::CompressType &compressType,
+                                      const std::map<std::string, AddressPtr> &compress_feature_maps) {
   if (fbb == nullptr) {
     MS_LOG(ERROR) << "Input fbb is nullptr.";
     return;
@@ -156,12 +183,40 @@ void GetModelKernel::BuildGetModelRsp(const std::shared_ptr<FBBuilder> &fbb, con
   }
   auto fbs_feature_maps_vector = fbb->CreateVector(fbs_feature_maps);
 
+  // construct compress feature maps with fbs
+  std::vector<flatbuffers::Offset<schema::CompressFeatureMap>> fbs_compress_feature_maps;
+  for (const auto &compress_feature_map : compress_feature_maps) {
+    if (compress_feature_map.first.find(kMinVal) != string::npos ||
+        compress_feature_map.first.find(kMaxVal) != string::npos) {
+      continue;
+    }
+    auto fbs_compress_weight_fullname = fbb->CreateString(compress_feature_map.first);
+    auto fbs_compress_weight_data = fbb->CreateVector(reinterpret_cast<int8_t *>(compress_feature_map.second->addr),
+                                                      compress_feature_map.second->size / sizeof(int8_t));
+
+    const std::string min_val_name = compress_feature_map.first + "." + kMinVal;
+    const std::string max_val_name = compress_feature_map.first + "." + kMaxVal;
+
+    const AddressPtr min_val_ptr = compress_feature_maps.at(min_val_name);
+    const AddressPtr max_val_ptr = compress_feature_maps.at(max_val_name);
+
+    float *fbs_min_val_ptr = reinterpret_cast<float *>(min_val_ptr->addr);
+    float *fbs_max_val_ptr = reinterpret_cast<float *>(max_val_ptr->addr);
+    auto fbs_compress_feature_map = schema::CreateCompressFeatureMap(
+      *(fbb.get()), fbs_compress_weight_fullname, fbs_compress_weight_data, *fbs_min_val_ptr, *fbs_max_val_ptr);
+
+    fbs_compress_feature_maps.push_back(fbs_compress_feature_map);
+  }
+  auto fbs_compress_feature_maps_vector = fbb->CreateVector(fbs_compress_feature_maps);
+
   schema::ResponseGetModelBuilder rsp_get_model_builder(*(fbb.get()));
   rsp_get_model_builder.add_retcode(static_cast<int>(retcode));
   rsp_get_model_builder.add_reason(fbs_reason);
   rsp_get_model_builder.add_iteration(static_cast<int>(iter));
   rsp_get_model_builder.add_feature_map(fbs_feature_maps_vector);
   rsp_get_model_builder.add_timestamp(fbs_timestamp);
+  rsp_get_model_builder.add_download_compress_type(compressType);
+  rsp_get_model_builder.add_compress_feature_map(fbs_compress_feature_maps_vector);
   auto rsp_get_model = rsp_get_model_builder.Finish();
   fbb->Finish(rsp_get_model);
   return;

mindspore/ccsrc/fl/server/kernel/round/get_model_kernel.h

@@ -25,6 +25,7 @@
 #include "fl/server/executor.h"
 #include "fl/server/kernel/round/round_kernel.h"
 #include "fl/server/kernel/round/round_kernel_factory.h"
+#include "fl/compression/encode_executor.h"
 
 namespace mindspore {
 namespace fl {
@@ -44,7 +45,9 @@ class GetModelKernel : public RoundKernel {
   void GetModel(const schema::RequestGetModel *get_model_req, const std::shared_ptr<ps::core::MessageHandler> &message);
   void BuildGetModelRsp(const std::shared_ptr<FBBuilder> &fbb, const schema::ResponseCode retcode,
                         const std::string &reason, const size_t iter,
-                        const std::map<std::string, AddressPtr> &feature_maps, const std::string &timestamp);
+                        const std::map<std::string, AddressPtr> &feature_maps, const std::string &timestamp,
+                        const schema::CompressType &compressType = schema::CompressType_NO_COMPRESS,
+                        const std::map<std::string, AddressPtr> &compress_feature_maps = {});
 
   // The executor is for getting model for getModel request.
   Executor *executor_;

mindspore/ccsrc/fl/server/kernel/round/start_fl_job_kernel.cc

@@ -126,10 +126,19 @@ bool StartFLJobKernel::Launch(const uint8_t *req_data, size_t len,
   IncreaseAcceptClientNum();
   auto curr_iter_num = LocalMetaStore::GetInstance().curr_iter_num();
   auto last_iteration = curr_iter_num - 1;
-  auto cache = ModelStore::GetInstance().GetModelResponseCache(name_, curr_iter_num, last_iteration);
+  auto download_compress_types = start_fl_job_req->download_compress_types();
+  schema::CompressType compressType =
+    mindspore::fl::compression::CompressExecutor::GetInstance().GetCompressType(download_compress_types);
+  std::string compress_type;
+  if (compressType == schema::CompressType_QUANT) {
+    compress_type = kQuant;
+  } else {
+    compress_type = kNoCompress;
+  }
+  auto cache = ModelStore::GetInstance().GetModelResponseCache(name_, curr_iter_num, last_iteration, compress_type);
   if (cache == nullptr) {
-    StartFLJob(fbb);
-    cache = ModelStore::GetInstance().StoreModelResponseCache(name_, curr_iter_num, last_iteration,
+    StartFLJob(fbb, device_meta, start_fl_job_req);
+    cache = ModelStore::GetInstance().StoreModelResponseCache(name_, curr_iter_num, last_iteration, compress_type,
                                                               fbb->GetBufferPointer(), fbb->GetSize());
     if (cache == nullptr) {
       SendResponseMsg(message, fbb->GetBufferPointer(), fbb->GetSize());
@@ -303,22 +312,40 @@ ResultCode StartFLJobKernel::CountForStartFLJob(const std::shared_ptr<FBBuilder>
   return ResultCode::kSuccess;
 }
 
-void StartFLJobKernel::StartFLJob(const std::shared_ptr<FBBuilder> &fbb) {
+void StartFLJobKernel::StartFLJob(const std::shared_ptr<FBBuilder> &fbb, const DeviceMeta &,
+                                  const schema::RequestFLJob *start_fl_job_req) {
   size_t last_iteration = LocalMetaStore::GetInstance().curr_iter_num() - 1;
-  auto feature_maps = ModelStore::GetInstance().GetModelByIterNum(last_iteration);
-  if (feature_maps.empty()) {
-    MS_LOG(WARNING) << "The feature map for startFLJob is empty.";
+
+  std::map<std::string, AddressPtr> feature_maps = {};
+  std::map<std::string, AddressPtr> compress_feature_maps = {};
+
+  // Only download compress weights if client support.
+  auto download_compress_types = start_fl_job_req->download_compress_types();
+  schema::CompressType compressType =
+    mindspore::fl::compression::CompressExecutor::GetInstance().GetCompressType(download_compress_types);
+  if (compressType == schema::CompressType_NO_COMPRESS) {
+    feature_maps = ModelStore::GetInstance().GetModelByIterNum(last_iteration);
+    if (feature_maps.empty()) {
+      MS_LOG(WARNING) << "The feature map for startFLJob is empty.";
+    }
+  } else {
+    if (mindspore::fl::compression::CompressExecutor::GetInstance().EnableCompressWeight(compressType)) {
+      compress_feature_maps = ModelStore::GetInstance().GetCompressModelByIterNum(last_iteration, compressType);
+    }
   }
+
   BuildStartFLJobRsp(fbb, schema::ResponseCode_SUCCEED, "success", true,
                      std::to_string(LocalMetaStore::GetInstance().value<uint64_t>(kCtxIterationNextRequestTimestamp)),
-                     feature_maps);
+                     feature_maps, compressType, compress_feature_maps);
   return;
 }
 
 void StartFLJobKernel::BuildStartFLJobRsp(const std::shared_ptr<FBBuilder> &fbb, const schema::ResponseCode retcode,
                                           const std::string &reason, const bool is_selected,
                                           const std::string &next_req_time,
-                                          std::map<std::string, AddressPtr> feature_maps) {
+                                          const std::map<std::string, AddressPtr> &feature_maps,
+                                          const schema::CompressType &compressType,
+                                          const std::map<std::string, AddressPtr> &compress_feature_maps) {
   if (fbb == nullptr) {
     MS_LOG(WARNING) << "Input fbb is nullptr.";
     return;
@@ -350,6 +377,12 @@ void StartFLJobKernel::BuildStartFLJobRsp(const std::shared_ptr<FBBuilder> &fbb,
   auto cipher_public_params =
     schema::CreateCipherPublicParams(*fbb.get(), encrypt_type, pw_params, dp_params, ds_params);
 #endif
+  schema::CompressType upload_compress_type;
+  if (ps::PSContext::instance()->upload_compress_type() == kDiffSparseQuant) {
+    upload_compress_type = schema::CompressType_DIFF_SPARSE_QUANT;
+  } else {
+    upload_compress_type = schema::CompressType_NO_COMPRESS;
+  }
 
   schema::FLPlanBuilder fl_plan_builder(*(fbb.get()));
   fl_plan_builder.add_fl_name(fbs_fl_name);
@@ -375,6 +408,33 @@ void StartFLJobKernel::BuildStartFLJobRsp(const std::shared_ptr<FBBuilder> &fbb,
   }
   auto fbs_feature_maps_vector = fbb->CreateVector(fbs_feature_maps);
 
+  // construct compress feature maps with fbs
+  std::vector<flatbuffers::Offset<schema::CompressFeatureMap>> fbs_compress_feature_maps;
+  for (const auto &compress_feature_map : compress_feature_maps) {
+    if (compressType == schema::CompressType_QUANT) {
+      if (compress_feature_map.first.find(kMinVal) != string::npos ||
+          compress_feature_map.first.find(kMaxVal) != string::npos) {
+        continue;
+      }
+      auto fbs_compress_weight_fullname = fbb->CreateString(compress_feature_map.first);
+      auto fbs_compress_weight_data = fbb->CreateVector(reinterpret_cast<int8_t *>(compress_feature_map.second->addr),
+                                                        compress_feature_map.second->size / sizeof(int8_t));
+
+      const std::string min_val_name = compress_feature_map.first + "." + kMinVal;
+      const std::string max_val_name = compress_feature_map.first + "." + kMaxVal;
+
+      const AddressPtr min_val_ptr = compress_feature_maps.at(min_val_name);
+      const AddressPtr max_val_ptr = compress_feature_maps.at(max_val_name);
+
+      float *fbs_min_val_ptr = reinterpret_cast<float *>(min_val_ptr->addr);
+      float *fbs_max_val_ptr = reinterpret_cast<float *>(max_val_ptr->addr);
+      auto fbs_compress_feature_map = schema::CreateCompressFeatureMap(
+        *(fbb.get()), fbs_compress_weight_fullname, fbs_compress_weight_data, *fbs_min_val_ptr, *fbs_max_val_ptr);
+      fbs_compress_feature_maps.push_back(fbs_compress_feature_map);
+    }
+  }
+  auto fbs_compress_feature_maps_vector = fbb->CreateVector(fbs_compress_feature_maps);
+
   schema::ResponseFLJobBuilder rsp_fl_job_builder(*(fbb.get()));
   rsp_fl_job_builder.add_retcode(static_cast<int>(retcode));
   rsp_fl_job_builder.add_reason(fbs_reason);
@@ -383,6 +443,10 @@ void StartFLJobKernel::BuildStartFLJobRsp(const std::shared_ptr<FBBuilder> &fbb,
   rsp_fl_job_builder.add_next_req_time(fbs_next_req_time);
   rsp_fl_job_builder.add_fl_plan_config(fbs_fl_plan);
   rsp_fl_job_builder.add_feature_map(fbs_feature_maps_vector);
+  rsp_fl_job_builder.add_download_compress_type(compressType);
+  rsp_fl_job_builder.add_compress_feature_map(fbs_compress_feature_maps_vector);
+  rsp_fl_job_builder.add_upload_compress_type(upload_compress_type);
+  rsp_fl_job_builder.add_upload_sparse_rate(ps::PSContext::instance()->upload_sparse_rate());
   auto rsp_fl_job = rsp_fl_job_builder.Finish();
   fbb->Finish(rsp_fl_job);
   return;

mindspore/ccsrc/fl/server/kernel/round/start_fl_job_kernel.h

@@ -25,6 +25,9 @@
 #include "fl/server/executor.h"
 #include "fl/server/kernel/round/round_kernel.h"
 #include "fl/server/kernel/round/round_kernel_factory.h"
+#include "schema/fl_job_generated.h"
+#include "schema/cipher_generated.h"
+#include "fl/compression/encode_executor.h"
 
 namespace mindspore {
 namespace fl {
@@ -56,7 +59,8 @@ class StartFLJobKernel : public RoundKernel {
   // Distributed count service counts for startFLJob.
   ResultCode CountForStartFLJob(const std::shared_ptr<FBBuilder> &fbb, const schema::RequestFLJob *start_fl_job_req);
 
-  void StartFLJob(const std::shared_ptr<FBBuilder> &fbb);
+  void StartFLJob(const std::shared_ptr<FBBuilder> &fbb, const DeviceMeta &device_meta,
+                  const schema::RequestFLJob *start_fl_job_req);
 
   bool JudgeFLJobCert(const std::shared_ptr<FBBuilder> &fbb, const schema::RequestFLJob *start_fl_job_req);
 
@@ -65,7 +69,9 @@ class StartFLJobKernel : public RoundKernel {
   // Build response for startFLJob round no matter success or failure.
   void BuildStartFLJobRsp(const std::shared_ptr<FBBuilder> &fbb, const schema::ResponseCode retcode,
                           const std::string &reason, const bool is_selected, const std::string &next_req_time,
-                          std::map<std::string, AddressPtr> feature_maps = {});
+                          const std::map<std::string, AddressPtr> &feature_maps = {},
+                          const schema::CompressType &compressType = schema::CompressType_NO_COMPRESS,
+                          const std::map<std::string, AddressPtr> &compress_feature_maps = {});
 
   // The executor is for getting the initial model for startFLJob request.
   Executor *executor_;

mindspore/ccsrc/fl/server/kernel/round/update_model_kernel.cc

@@ -201,23 +201,27 @@ ResultCode UpdateModelKernel::VerifyUpdateModel(const schema::RequestUpdateModel
   }
 
   std::unordered_map<std::string, size_t> feature_map;
-  auto upload_feature_map = update_model_req->feature_map();
-  MS_ERROR_IF_NULL_W_RET_VAL(upload_feature_map, ResultCode::kFail);
-  for (uint32_t i = 0; i < upload_feature_map->size(); i++) {
-    const auto &item = upload_feature_map->Get(i);
-    MS_ERROR_IF_NULL_W_RET_VAL(item, ResultCode::kFail);
-    MS_ERROR_IF_NULL_W_RET_VAL(item->weight_fullname(), ResultCode::kFail);
-    MS_ERROR_IF_NULL_W_RET_VAL(item->data(), ResultCode::kFail);
-
-    std::string weight_full_name = item->weight_fullname()->str();
-    size_t weight_size = item->data()->size() * sizeof(float);
-    feature_map[weight_full_name] = weight_size;
+  if (ps::PSContext::instance()->upload_compress_type() != kDiffSparseQuant) {
+    auto upload_feature_map = update_model_req->feature_map();
+    MS_ERROR_IF_NULL_W_RET_VAL(upload_feature_map, ResultCode::kFail);
+    for (uint32_t i = 0; i < upload_feature_map->size(); i++) {
+      const auto &item = upload_feature_map->Get(i);
+      MS_ERROR_IF_NULL_W_RET_VAL(item, ResultCode::kFail);
+      MS_ERROR_IF_NULL_W_RET_VAL(item->weight_fullname(), ResultCode::kFail);
+      MS_ERROR_IF_NULL_W_RET_VAL(item->data(), ResultCode::kFail);
+
+      std::string weight_full_name = item->weight_fullname()->str();
+      size_t weight_size = item->data()->size() * sizeof(float);
+      feature_map[weight_full_name] = weight_size;
+    }
   }
 
   bool verifyFeatureMapIsSuccess;
   if (ps::PSContext::instance()->encrypt_type() == ps::kDSEncryptType && update_model_req->sign() != 0) {
     MS_ERROR_IF_NULL_W_RET_VAL(update_model_req->index_array(), ResultCode::kFail);
     verifyFeatureMapIsSuccess = VerifySignDSFeatureMap(feature_map, update_model_req);
+  } else if (ps::PSContext::instance()->upload_compress_type() == kDiffSparseQuant) {
+    verifyFeatureMapIsSuccess = VerifyUploadCompressFeatureMap(update_model_req);
   } else {
     verifyFeatureMapIsSuccess = LocalMetaStore::GetInstance().verifyAggregationFeatureMap(feature_map);
   }
@@ -280,6 +284,45 @@ bool UpdateModelKernel::VerifySignDSFeatureMap(const std::unordered_map<std::str
   return true;
 }
 
+bool UpdateModelKernel::VerifyUploadCompressFeatureMap(const schema::RequestUpdateModel *update_model_req) {
+  auto &aggregation_feature_map_ = LocalMetaStore::GetInstance().aggregation_feature_map();
+  auto upload_sparse_rate = update_model_req->upload_sparse_rate();
+  if (upload_sparse_rate != ps::PSContext::instance()->upload_sparse_rate()) {
+    MS_LOG(WARNING) << "The upload_sparse_rate must be equal to the setting in context.";
+    return false;
+  }
+  auto fbs_name_vec = update_model_req->name_vec();
+  if (fbs_name_vec == nullptr) {
+    MS_LOG(WARNING) << "The name_vec is null.";
+    return false;
+  }
+  if (fbs_name_vec->size() == 0) {
+    MS_LOG(WARNING) << "The size of name_vec must be larger than 0.";
+    return false;
+  }
+  if (fbs_name_vec->size() > aggregation_feature_map_.size()) {
+    MS_LOG(WARNING) << "The size of name_vec must be smaller than model in server.";
+    return false;
+  }
+  for (size_t i = 0; i < fbs_name_vec->size(); ++i) {
+    std::string name = fbs_name_vec->Get(i)->str();
+    if (aggregation_feature_map_.count(name) == 0) {
+      MS_LOG(WARNING) << "The upload name: " << name << " is not in model in server.";
+      return false;
+    }
+  }
+  auto fbs_compress_feature_map = update_model_req->compress_feature_map();
+  if (fbs_compress_feature_map == nullptr) {
+    MS_LOG(WARNING) << "The upload compress feature map is null.";
+    return false;
+  }
+  if (fbs_compress_feature_map->size() == 0) {
+    MS_LOG(WARNING) << "The upload compress feature map is empty.";
+    return false;
+  }
+  return true;
+}
+
 ResultCode UpdateModelKernel::UpdateModel(const schema::RequestUpdateModel *update_model_req,
                                           const std::shared_ptr<FBBuilder> &fbb, const DeviceMeta &device_meta) {
   MS_ERROR_IF_NULL_W_RET_VAL(update_model_req, ResultCode::kFail);
@@ -292,6 +335,8 @@ ResultCode UpdateModelKernel::UpdateModel(const schema::RequestUpdateModel *upda
   std::map<std::string, UploadData> feature_map;
   if (ps::PSContext::instance()->encrypt_type() == ps::kDSEncryptType) {
     feature_map = ParseSignDSFeatureMap(update_model_req, data_size, &weight_map);
+  } else if (ps::PSContext::instance()->upload_compress_type() == kDiffSparseQuant) {
+    feature_map = ParseUploadCompressFeatureMap(update_model_req, data_size, &weight_map);
   } else {
     feature_map = ParseFeatureMap(update_model_req);
   }
@@ -397,6 +442,89 @@ std::map<std::string, UploadData> UpdateModelKernel::ParseSignDSFeatureMap(
   return feature_map;
 }
 
+std::map<std::string, UploadData> UpdateModelKernel::ParseUploadCompressFeatureMap(
+  const schema::RequestUpdateModel *update_model_req, size_t data_size,
+  std::map<std::string, std::vector<float>> *weight_map) {
+  MS_ERROR_IF_NULL_W_RET_VAL(update_model_req, {});
+  std::map<std::string, UploadData> feature_map;
+  schema::CompressType upload_compress_type = update_model_req->upload_compress_type();
+  upload_compress_type =
+    mindspore::fl::compression::DecodeExecutor::GetInstance().GetCompressType(upload_compress_type);
+  MS_LOG(INFO) << "This schema upload compress type is: " << upload_compress_type;
+  if (upload_compress_type != schema::CompressType_NO_COMPRESS) {
+    MS_LOG(INFO) << "This upload compress type is DIFF_SPARSE_QUANT.";
+    feature_map = DecodeFeatureMap(weight_map, update_model_req, upload_compress_type, data_size);
+    return feature_map;
+  }
+  MS_LOG(INFO) << "This upload compress type is NO_COMPRESS.";
+  // Some clients upload origin weights.
+  auto fbs_feature_map = update_model_req->feature_map();
+  MS_ERROR_IF_NULL_W_RET_VAL(fbs_feature_map, feature_map);
+  for (uint32_t i = 0; i < fbs_feature_map->size(); i++) {
+    std::string weight_full_name = fbs_feature_map->Get(i)->weight_fullname()->str();
+    float *weight_data = const_cast<float *>(fbs_feature_map->Get(i)->data()->data());
+    size_t weight_size = fbs_feature_map->Get(i)->data()->size() * sizeof(float);
+    UploadData upload_data;
+    upload_data[kNewWeight].addr = weight_data;
+    upload_data[kNewWeight].size = weight_size;
+    feature_map[weight_full_name] = upload_data;
+  }
+  return feature_map;
+}
+
+std::map<std::string, UploadData> UpdateModelKernel::DecodeFeatureMap(
+  std::map<std::string, std::vector<float>> *weight_map, const schema::RequestUpdateModel *update_model_req,
+  schema::CompressType upload_compress_type, size_t data_size) {
+  std::map<std::string, UploadData> feature_map;
+
+  // Get and set decode hyper parameters.
+  auto seed = update_model_req->iteration();
+  MS_LOG(INFO) << "The seed for compression is: " << seed;
+  auto upload_sparse_rate = update_model_req->upload_sparse_rate();
+  MS_LOG(INFO) << "The upload_sparse_rate for compression is: " << upload_sparse_rate;
+  // Get name vector.
+  auto fbs_name_vec = update_model_req->name_vec();
+  std::vector<std::string> name_vec;
+  for (size_t i = 0; i < fbs_name_vec->size(); ++i) {
+    name_vec.emplace_back(fbs_name_vec->Get(i)->str());
+  }
+
+  // Parameter process for decode.
+  auto fbs_compress_feature_map = update_model_req->compress_feature_map();
+  std::vector<mindspore::fl::compression::CompressFeatureMap> compress_feature_maps;
+  for (size_t i = 0; i < fbs_compress_feature_map->size(); ++i) {
+    mindspore::fl::compression::CompressFeatureMap compress_feature_map;
+    int8_t *compress_weight_data = const_cast<int8_t *>(fbs_compress_feature_map->Get(i)->compress_data()->data());
+    size_t compress_weight_size = fbs_compress_feature_map->Get(i)->compress_data()->size();
+    MS_LOG(INFO) << "The compress weight size: " << compress_weight_size;
+    for (size_t j = 0; j < compress_weight_size; ++j) {
+      compress_feature_map.compress_data.emplace_back(compress_weight_data[j]);
+    }
+    compress_feature_map.min_val = fbs_compress_feature_map->Get(i)->min_val();
+    compress_feature_map.max_val = fbs_compress_feature_map->Get(i)->max_val();
+    MS_LOG(INFO) << "Min value: " << compress_feature_map.min_val;
+    MS_LOG(INFO) << "Max value: " << compress_feature_map.max_val;
+    compress_feature_maps.emplace_back(compress_feature_map);
+  }
+
+  // Decode.
+  bool status = mindspore::fl::compression::DecodeExecutor::GetInstance().Decode(
+    weight_map, compress_feature_maps, upload_compress_type, upload_sparse_rate, seed, name_vec, data_size);
+  if (status) {
+    for (size_t i = 0; i < name_vec.size(); ++i) {
+      std::string weight_full_name = name_vec[i];
+      size_t weight_size = (*weight_map)[weight_full_name].size() * sizeof(float);
+      UploadData upload_data;
+      upload_data[kNewWeight].addr = (*weight_map)[weight_full_name].data();
+      upload_data[kNewWeight].size = weight_size;
+      feature_map[weight_full_name] = upload_data;
+    }
+    return feature_map;
+  }
+  MS_LOG(WARNING) << "Decode failed!";
+  return feature_map;
+}
+
 ResultCode UpdateModelKernel::CountForAggregation(const std::string &req_fl_id) {
   std::string count_reason = "";
   if (!DistributedCountService::GetInstance().Count(kCountForAggregation, req_fl_id, &count_reason)) {

mindspore/ccsrc/fl/server/kernel/round/update_model_kernel.h

@@ -30,6 +30,9 @@
 #ifdef ENABLE_ARMOUR
 #include "fl/armour/cipher/cipher_meta_storage.h"
 #endif
+#include "fl/compression/decode_executor.h"
+#include "schema/fl_job_generated.h"
+#include "schema/cipher_generated.h"
 
 namespace mindspore {
 namespace fl {
@@ -64,8 +67,12 @@ class UpdateModelKernel : public RoundKernel {
   std::map<std::string, UploadData> ParseSignDSFeatureMap(const schema::RequestUpdateModel *update_model_req,
                                                           size_t data_size,
                                                           std::map<std::string, std::vector<float>> *weight_map);
+  std::map<std::string, UploadData> ParseUploadCompressFeatureMap(
+    const schema::RequestUpdateModel *update_model_req, size_t data_size,
+    std::map<std::string, std::vector<float>> *weight_map);
   bool VerifySignDSFeatureMap(const std::unordered_map<std::string, size_t> &model,
                               const schema::RequestUpdateModel *update_model_req);
+  bool VerifyUploadCompressFeatureMap(const schema::RequestUpdateModel *update_model_req);
   ResultCode CountForUpdateModel(const std::shared_ptr<FBBuilder> &fbb,
                                  const schema::RequestUpdateModel *update_model_req);
   sigVerifyResult VerifySignature(const schema::RequestUpdateModel *update_model_req);
@@ -78,6 +85,11 @@ class UpdateModelKernel : public RoundKernel {
 
   // The time window of one iteration.
   size_t iteration_time_window_{0};
+
+  // Decode functions of compression.
+  std::map<std::string, UploadData> DecodeFeatureMap(std::map<std::string, std::vector<float>> *weight_map,
+                                                     const schema::RequestUpdateModel *update_model_req,
+                                                     schema::CompressType upload_compress_type, size_t data_size);
 };
 }  // namespace kernel
 }  // namespace server

mindspore/ccsrc/fl/server/memory_register.cc

@@ -44,6 +44,11 @@ void MemoryRegister::StoreCharArray(std::unique_ptr<char[]> *array) {
   MS_ERROR_IF_NULL_WO_RET_VAL(array);
   char_arrays_.push_back(std::move(*array));
 }
+
+void MemoryRegister::StoreFloat32(std::unique_ptr<float> *param) {
+  MS_ERROR_IF_NULL_WO_RET_VAL(param);
+  float_params_.push_back(std::move(*param));
+}
 }  // namespace server
 }  // namespace fl
 }  // namespace mindspore

mindspore/ccsrc/fl/server/memory_register.h

@@ -24,6 +24,7 @@
 #include <utility>
 #include <typeinfo>
 #include "fl/server/common.h"
+#include "fl/compression/encode_executor.h"
 
 namespace mindspore {
 namespace fl {
@@ -70,6 +71,25 @@ class MemoryRegister {
     return;
   }
 
+  template <typename T>
+  void RegisterParameter(const std::string &name, std::unique_ptr<T> *param, size_t size) {
+    MS_EXCEPTION_IF_NULL(param);
+    void *data = param->get();
+    AddressPtr addressPtr = std::make_shared<Address>();
+    addressPtr->addr = data;
+    addressPtr->size = size;
+    if (typeid(T) == typeid(float)) {
+      auto float_param = CastUniqueParamPtr<float, T>(param);
+      StoreFloat32(&float_param);
+    } else {
+      MS_LOG(ERROR) << "MemoryRegister does not support type " << typeid(T).name();
+      return;
+    }
+
+    RegisterAddressPtr(name, addressPtr);
+    return;
+  }
+
  private:
   std::map<std::string, AddressPtr> addresses_;
   std::vector<std::unique_ptr<float[]>> float_arrays_;
@@ -86,6 +106,15 @@ class MemoryRegister {
   std::unique_ptr<T[]> CastUniquePtr(std::unique_ptr<S[]> *array) {
     return std::unique_ptr<T[]>{reinterpret_cast<T *>(array->release())};
   }
+
+  std::vector<std::unique_ptr<float>> float_params_;
+
+  void StoreFloat32(std::unique_ptr<float> *array);
+
+  template <typename T, typename S>
+  std::unique_ptr<T> CastUniqueParamPtr(std::unique_ptr<S> *param) {
+    return std::unique_ptr<T>{reinterpret_cast<T *>(param->release())};
+  }
 };
 }  // namespace server
 }  // namespace fl

mindspore/ccsrc/fl/server/model_store.cc

@@ -19,6 +19,7 @@
 #include <string>
 #include <memory>
 #include "fl/server/executor.h"
+#include "pipeline/jit/parse/parse.h"
 #include "include/common/utils/python_adapter.h"
 
 namespace mindspore {
@@ -33,6 +34,10 @@ void ModelStore::Initialize(uint32_t rank_id, uint32_t max_count) {
   max_model_count_ = max_count;
   initial_model_ = AssignNewModelMemory();
   iteration_to_model_[kInitIterationNum] = initial_model_;
+  std::map<std::string, AddressPtr> model = Executor::GetInstance().GetModel();
+  for (const auto &item : mindspore::fl::compression::kCompressTypeMap) {
+    iteration_to_compress_model_[kInitIterationNum][item.first] = AssignNewCompressModelMemory(item.first, model);
+  }
   model_size_ = ComputeModelSize();
   MS_LOG(INFO) << "Model store checkpoint dir is: " << ps::PSContext::instance()->checkpoint_dir();
 }
@@ -101,6 +106,24 @@ std::map<std::string, AddressPtr> ModelStore::GetModelByIterNum(size_t iteration
   return model;
 }
 
+std::map<std::string, AddressPtr> ModelStore::GetCompressModelByIterNum(size_t iteration,
+                                                                        schema::CompressType compressType) {
+  std::unique_lock<std::mutex> lock(model_mtx_);
+  std::map<std::string, AddressPtr> compressModel = {};
+  if (iteration_to_compress_model_.count(iteration) == 0) {
+    MS_LOG(ERROR) << "Compress Model for iteration " << iteration << " is not stored.";
+    return compressModel;
+  }
+  std::map<schema::CompressType, std::shared_ptr<MemoryRegister>> compress_model_map =
+    iteration_to_compress_model_[iteration];
+  if (compress_model_map.count(compressType) == 0) {
+    MS_LOG(ERROR) << "Compress Model for compress type " << compressType << " is not stored.";
+    return compressModel;
+  }
+  compressModel = iteration_to_compress_model_[iteration][compressType]->addresses();
+  return compressModel;
+}
+
 void ModelStore::Reset() {
   std::unique_lock<std::mutex> lock(model_mtx_);
   initial_model_ = iteration_to_model_.rbegin()->second;
@@ -114,6 +137,11 @@ const std::map<size_t, std::shared_ptr<MemoryRegister>> &ModelStore::iteration_t
   return iteration_to_model_;
 }
 
+const std::map<size_t, CompressTypeMap> &ModelStore::iteration_to_compress_model() {
+  std::unique_lock<std::mutex> lock(model_mtx_);
+  return iteration_to_compress_model_;
+}
+
 size_t ModelStore::model_size() const { return model_size_; }
 
 std::shared_ptr<MemoryRegister> ModelStore::AssignNewModelMemory() {
@@ -146,6 +174,86 @@ std::shared_ptr<MemoryRegister> ModelStore::AssignNewModelMemory() {
   return memory_register;
 }
 
+std::shared_ptr<MemoryRegister> ModelStore::AssignNewCompressModelMemory(
+  schema::CompressType compressType, const std::map<std::string, AddressPtr> &model) {
+  if (model.empty()) {
+    MS_LOG(EXCEPTION) << "Model feature map is empty.";
+    return nullptr;
+  }
+  std::map<string, std::vector<float>> feature_maps;
+  for (auto &feature_map : model) {
+    auto weight_fullname = feature_map.first;
+    auto weight_data = reinterpret_cast<float *>(feature_map.second->addr);
+    std::vector<float> weight_data_vector{weight_data, weight_data + feature_map.second->size / sizeof(float)};
+    feature_maps[weight_fullname] = weight_data_vector;
+  }
+
+  std::map<std::string, mindspore::fl::compression::CompressWeight> compressWeights;
+  bool status = mindspore::fl::compression::CompressExecutor::GetInstance().construct_compress_weight(
+    &compressWeights, feature_maps, compressType);
+  if (!status) {
+    MS_LOG(ERROR) << "Encode failed!";
+    return nullptr;
+  }
+
+  // Assign new memory for the compress model.
+  std::shared_ptr<MemoryRegister> memory_register = std::make_shared<MemoryRegister>();
+  MS_ERROR_IF_NULL_W_RET_VAL(memory_register, nullptr);
+  MS_LOG(INFO) << "Register compressWeight for compressType: " << schema::EnumNameCompressType(compressType);
+
+  for (const auto &compressWeight : compressWeights) {
+    if (compressType == schema::CompressType_QUANT) {
+      std::string compress_weight_name = compressWeight.first;
+      std::string min_val_name = compress_weight_name + "." + kMinVal;
+      std::string max_val_name = compress_weight_name + "." + kMaxVal;
+      size_t compress_weight_size = compressWeight.second.compress_data_len * sizeof(int8_t);
+      auto compress_weight_data = std::make_unique<char[]>(compress_weight_size);
+      auto src_data_size = compress_weight_size;
+      auto dst_data_size = compress_weight_size;
+      int ret =
+        memcpy_s(compress_weight_data.get(), dst_data_size, compressWeight.second.compress_data.data(), src_data_size);
+      if (ret != 0) {
+        MS_LOG(ERROR) << "memcpy_s error, errorno(" << ret << ")";
+        return nullptr;
+      }
+      memory_register->RegisterArray(compress_weight_name, &compress_weight_data, compress_weight_size);
+      size_t float_size = 1;
+      auto min_val_ptr = std::make_unique<float>(compressWeight.second.min_val);
+      auto max_val_ptr = std::make_unique<float>(compressWeight.second.max_val);
+
+      memory_register->RegisterParameter(min_val_name, &min_val_ptr, float_size);
+      memory_register->RegisterParameter(max_val_name, &max_val_ptr, float_size);
+    }
+  }
+  return memory_register;
+}
+
+void ModelStore::StoreCompressModelByIterNum(size_t iteration, const std::map<std::string, AddressPtr> &new_model) {
+  std::unique_lock<std::mutex> lock(model_mtx_);
+  if (iteration_to_compress_model_.count(iteration) != 0) {
+    MS_LOG(WARNING) << "Compress Model for iteration " << iteration << " is already stored";
+    return;
+  }
+  if (new_model.empty()) {
+    MS_LOG(ERROR) << "Compress Model feature map is empty.";
+    return;
+  }
+
+  iteration_to_compress_model_[iteration] = {};
+  if (iteration_to_compress_model_.size() >= max_model_count_) {
+    auto compress_model_map = iteration_to_compress_model_.begin()->second;
+    compress_model_map.clear();
+    (void)iteration_to_compress_model_.erase(iteration_to_compress_model_.begin());
+  }
+
+  for (const auto &item : mindspore::fl::compression::kCompressTypeMap) {
+    auto memory_register = AssignNewCompressModelMemory(item.first, new_model);
+    MS_ERROR_IF_NULL_WO_RET_VAL(memory_register);
+    iteration_to_compress_model_[iteration][item.first] = memory_register;
+  }
+  return;
+}
+
 size_t ModelStore::ComputeModelSize() {
   std::unique_lock<std::mutex> lock(model_mtx_);
   if (iteration_to_model_.empty()) {
@@ -179,13 +287,15 @@ void ModelStore::RelModelResponseCache(const void *data, size_t datalen, void *e
 
 std::shared_ptr<std::vector<uint8_t>> ModelStore::GetModelResponseCache(const string &round_name,
                                                                         size_t cur_iteration_num,
-                                                                        size_t model_iteration_num) {
+                                                                        size_t model_iteration_num,
+                                                                        const std::string &compress_type) {
   std::unique_lock<std::mutex> lock(model_response_cache_lock_);
-  auto it = std::find_if(model_response_cache_.begin(), model_response_cache_.end(),
-                         [&round_name, cur_iteration_num, model_iteration_num](const HttpResponseModelCache &item) {
-                           return item.round_name == round_name && item.cur_iteration_num == cur_iteration_num &&
-                                  item.model_iteration_num == model_iteration_num;
-                         });
+  auto it = std::find_if(
+    model_response_cache_.begin(), model_response_cache_.end(),
+    [&round_name, cur_iteration_num, model_iteration_num, &compress_type](const HttpResponseModelCache &item) {
+      return item.round_name == round_name && item.cur_iteration_num == cur_iteration_num &&
+             item.model_iteration_num == model_iteration_num && item.compress_type == compress_type;
+    });
   if (it == model_response_cache_.end()) {
     return nullptr;
   }
@@ -196,14 +306,16 @@ std::shared_ptr<std::vector<uint8_t>> ModelStore::GetModelResponseCache(const st
 
 std::shared_ptr<std::vector<uint8_t>> ModelStore::StoreModelResponseCache(const string &round_name,
                                                                           size_t cur_iteration_num,
-                                                                          size_t model_iteration_num, const void *data,
-                                                                          size_t datalen) {
+                                                                          size_t model_iteration_num,
+                                                                          const std::string &compress_type,
+                                                                          const void *data, size_t datalen) {
   std::unique_lock<std::mutex> lock(model_response_cache_lock_);
-  auto it = std::find_if(model_response_cache_.begin(), model_response_cache_.end(),
-                         [&round_name, cur_iteration_num, model_iteration_num](const HttpResponseModelCache &item) {
-                           return item.round_name == round_name && item.cur_iteration_num == cur_iteration_num &&
-                                  item.model_iteration_num == model_iteration_num;
-                         });
+  auto it = std::find_if(
+    model_response_cache_.begin(), model_response_cache_.end(),
+    [&round_name, cur_iteration_num, model_iteration_num, &compress_type](const HttpResponseModelCache &item) {
+      return item.round_name == round_name && item.cur_iteration_num == cur_iteration_num &&
+             item.model_iteration_num == model_iteration_num && item.compress_type == compress_type;
+    });
   if (it != model_response_cache_.end()) {
     it->reference_count += 1;
     total_add_reference_count += 1;
@@ -223,6 +335,7 @@ std::shared_ptr<std::vector<uint8_t>> ModelStore::StoreModelResponseCache(const
   item.round_name = round_name;
   item.cur_iteration_num = cur_iteration_num;
   item.model_iteration_num = model_iteration_num;
+  item.compress_type = compress_type;
   item.cache = cache;
   item.reference_count = 1;
   total_add_reference_count += 1;

mindspore/ccsrc/fl/server/model_store.h

@@ -25,6 +25,7 @@
 #include "fl/server/common.h"
 #include "fl/server/memory_register.h"
 #include "fl/server/executor.h"
+#include "fl/compression/encode_executor.h"
 #include "fl/server/local_meta_store.h"
 
 namespace mindspore {
@@ -36,6 +37,9 @@ constexpr size_t kInitIterationNum = 0;
 // The initial iteration number after ModelStore is reset.
 constexpr size_t kResetInitialIterNum = 1;
 
+// The compress type map.
+using CompressTypeMap = std::map<schema::CompressType, std::shared_ptr<MemoryRegister>>;
+
 // Server framework use ModelStore to store and query models.
 // ModelStore stores multiple models because worker could get models of the previous iterations.
 class ModelStore {
@@ -64,15 +68,25 @@ class ModelStore {
   // Returns the model size, which could be calculated at the initializing phase.
   size_t model_size() const;
 
+  // Get compress model of the given iteration.
+  std::map<std::string, AddressPtr> GetCompressModelByIterNum(size_t iteration, schema::CompressType compressType);
+
+  const std::map<size_t, std::map<schema::CompressType, std::shared_ptr<MemoryRegister>>>
+    &iteration_to_compress_model();
+
+  void StoreCompressModelByIterNum(size_t iteration, const std::map<std::string, AddressPtr> &new_model);
+
   static void RelModelResponseCache(const void *data, size_t datalen, void *extra);
   std::shared_ptr<std::vector<uint8_t>> GetModelResponseCache(const std::string &round_name, size_t cur_iteration_num,
-                                                              size_t model_iteration_num);
+                                                              size_t model_iteration_num,
+                                                              const std::string &compress_type);
   std::shared_ptr<std::vector<uint8_t>> StoreModelResponseCache(const std::string &round_name, size_t cur_iteration_num,
-                                                                size_t model_iteration_num, const void *data,
+                                                                size_t model_iteration_num,
+                                                                const std::string &compress_type, const void *data,
                                                                 size_t datalen);
 
  private:
-  ModelStore() : max_model_count_(0), model_size_(0), iteration_to_model_({}) {}
+  ModelStore() : max_model_count_(0), model_size_(0), iteration_to_model_({}), iteration_to_compress_model_({}) {}
   ~ModelStore() = default;
   ModelStore(const ModelStore &) = delete;
   ModelStore &operator=(const ModelStore &) = delete;
@@ -83,6 +97,9 @@ class ModelStore {
   // model_size_.
   std::shared_ptr<MemoryRegister> AssignNewModelMemory();
 
+  std::shared_ptr<MemoryRegister> AssignNewCompressModelMemory(schema::CompressType compressType,
+                                                               const std::map<std::string, AddressPtr> &model);
+
   // Calculate the model size. This method should be called after iteration_to_model_ is initialized.
   size_t ComputeModelSize();
 
@@ -95,12 +112,17 @@ class ModelStore {
   // The number of all models stored is max_model_count_.
   std::mutex model_mtx_;
   std::map<size_t, std::shared_ptr<MemoryRegister>> iteration_to_model_;
+
+  // iteration -> (compress type -> compress model)
+  std::map<size_t, std::map<schema::CompressType, std::shared_ptr<MemoryRegister>>> iteration_to_compress_model_;
+
   uint32_t rank_id_;
 
   struct HttpResponseModelCache {
     std::string round_name;  // startFlJob, getModel
     size_t cur_iteration_num = 0;
     size_t model_iteration_num = 0;
+    std::string compress_type = kNoCompress;
     size_t reference_count = 0;
     std::shared_ptr<std::vector<uint8_t>> cache = nullptr;
   };

mindspore/ccsrc/pipeline/jit/init.cc

@@ -507,6 +507,12 @@ PYBIND11_MODULE(_c_expression, m) {
     .def("set_global_iteration_time_window", &PSContext::set_global_iteration_time_window,
          "Set global iteration time window.")
     .def("global_iteration_time_window", &PSContext::global_iteration_time_window, "Get global iteration time window.")
+    .def("set_upload_compress_type", &PSContext::set_upload_compress_type, "Set upload compress type.")
+    .def("upload_compress_type", &PSContext::upload_compress_type, "Get upload compress type.")
+    .def("set_upload_sparse_rate", &PSContext::set_upload_sparse_rate, "Set upload sparse rate.")
+    .def("upload_sparse_rate", &PSContext::upload_sparse_rate, "Get upload sparse rate.")
+    .def("set_download_compress_type", &PSContext::set_download_compress_type, "Set download compress type.")
+    .def("download_compress_type", &PSContext::download_compress_type, "Get download compress type.")
     .def("set_checkpoint_dir", &PSContext::set_checkpoint_dir, "Set server checkpoint directory.")
     .def("checkpoint_dir", &PSContext::checkpoint_dir, "Server checkpoint directory.");
   (void)m.def("_encrypt", &mindspore::pipeline::PyEncrypt, "Encrypt the data.");

mindspore/ccsrc/ps/ps_context.cc

@@ -550,6 +550,19 @@ void PSContext::set_global_iteration_time_window(const uint64_t &global_iteratio
 
 uint64_t PSContext::global_iteration_time_window() const { return global_iteration_time_window_; }
 
+void PSContext::set_upload_compress_type(const std::string &upload_compress_type) {
+  upload_compress_type_ = upload_compress_type;
+}
+std::string PSContext::upload_compress_type() const { return upload_compress_type_; }
+
+void PSContext::set_upload_sparse_rate(float upload_sparse_rate) { upload_sparse_rate_ = upload_sparse_rate; }
+float PSContext::upload_sparse_rate() const { return upload_sparse_rate_; }
+
+void PSContext::set_download_compress_type(const std::string &download_compress_type) {
+  download_compress_type_ = download_compress_type;
+}
+std::string PSContext::download_compress_type() const { return download_compress_type_; }
+
 std::string PSContext::checkpoint_dir() const { return checkpoint_dir_; }
 
 void PSContext::set_checkpoint_dir(const std::string &checkpoint_dir) { checkpoint_dir_ = checkpoint_dir; }

mindspore/ccsrc/ps/ps_context.h

@@ -40,6 +40,7 @@ constexpr char kPWEncryptType[] = "PW_ENCRYPT";
 constexpr char kStablePWEncryptType[] = "STABLE_PW_ENCRYPT";
 constexpr char kNotEncryptType[] = "NOT_ENCRYPT";
 constexpr char kDSEncryptType[] = "SIGNDS";
+constexpr char kNoCompressType[] = "NO_COMPRESS";
 
 // Use binary data to represent federated learning server's context so that we can judge which round resets the
 // iteration. From right to left, each bit stands for:
@@ -230,6 +231,15 @@ class PSContext {
   void set_global_iteration_time_window(const uint64_t &global_iteration_time_window);
   uint64_t global_iteration_time_window() const;
 
+  void set_upload_compress_type(const std::string &upload_compress_type);
+  std::string upload_compress_type() const;
+
+  void set_upload_sparse_rate(float upload_sparse_rate);
+  float upload_sparse_rate() const;
+
+  void set_download_compress_type(const std::string &download_compress_type);
+  std::string download_compress_type() const;
+
   std::string checkpoint_dir() const;
   void set_checkpoint_dir(const std::string &checkpoint_dir);
 
@@ -286,6 +296,9 @@ class PSContext {
         server_password_(""),
         http_url_prefix_(""),
         global_iteration_time_window_(3600000),
+        upload_compress_type_(kNoCompressType),
+        upload_sparse_rate_(0.4f),
+        download_compress_type_(kNoCompressType),
         checkpoint_dir_("") {}
   bool ps_enabled_;
   bool is_worker_;
@@ -419,6 +432,13 @@ class PSContext {
 
   // The time window of startFLJob round in millisecond.
   uint64_t global_iteration_time_window_;
+
+  // Hyper parameters for upload compression.
+  std::string upload_compress_type_;
+  float upload_sparse_rate_;
+  // Hyper parameters for download compression.
+  std::string download_compress_type_;
+
   // directory of server checkpoint
   std::string checkpoint_dir_;
 };

mindspore/lite/java/java/fl_client/src/main/java/com/mindspore/flclient/FLLiteClient.java

@@ -105,6 +105,16 @@ public class FLLiteClient {
         batchSize = flPlan.miniBatch();
         String serverMod = flPlan.serverMode();
         localFLParameter.setServerMod(serverMod);
+        // Get and set hyper parameters for compression.
+        byte uploadCompressType = flJob.uploadCompressType();
+        LOGGER.info(Common.addTag("[startFLJob] [compression] uploadCompressType: " + uploadCompressType));
+        localFLParameter.setUploadCompressType(uploadCompressType);
+        float uploadSparseRate = flJob.uploadSparseRate();
+        LOGGER.info(Common.addTag("[startFLJob] [compression] uploadSparseRate: " + uploadSparseRate));
+        localFLParameter.setUploadSparseRatio(uploadSparseRate);
+        int seed = flJob.iteration();
+        LOGGER.info(Common.addTag("[startFLJob] [compression] seed: " + seed));
+        localFLParameter.setSeed(seed);
         if (Common.checkFLName(flParameter.getFlName())) {
             deprecatedSetBatchSize(batchSize);
         } else {
@@ -446,7 +456,7 @@ public class FLLiteClient {
         return status;
     }
 
-    private Map<String, float[]> getFeatureMap() {
+    public Map<String, float[]> getFeatureMap() {
         Map<String, float[]> featureMap = new HashMap<>();
         if (Common.checkFLName(flParameter.getFlName())) {
             featureMap = deprecatedGetFeatureMap();
@@ -530,8 +540,7 @@ public class FLLiteClient {
                         localFLParameter.getEncryptLevel().toString() + "> : " + curStatus));
                 return curStatus;
             case DP_ENCRYPT:
-                // get the feature map before train
-                oldFeatureMap = getFeatureMap();
+                oldFeatureMap = localFLParameter.getOldFeatureMap();
                 curStatus = secureProtocol.setDPParameter(iteration, dpEps, dpDelta, dpNormClipAdapt, oldFeatureMap);
                 retCode = ResponseCode.SUCCEED;
                 if (curStatus != FLClientStatus.SUCCESS) {
@@ -542,8 +551,7 @@ public class FLLiteClient {
                 LOGGER.info(Common.addTag("[Encrypt] set parameters for DP_ENCRYPT!"));
                 return FLClientStatus.SUCCESS;
             case SIGNDS:
-                // get the feature map before train
-                oldFeatureMap = getFeatureMap();
+                oldFeatureMap = localFLParameter.getOldFeatureMap();
                 curStatus = secureProtocol.setDSParameter(signK, signEps, signThrRatio, signGlobalLr, signDimOut, oldFeatureMap);
                 retCode = ResponseCode.SUCCEED;
                 if (curStatus != FLClientStatus.SUCCESS) {

mindspore/lite/java/java/fl_client/src/main/java/com/mindspore/flclient/FLParameter.java

@@ -18,7 +18,9 @@ package com.mindspore.flclient;
 
 import static com.mindspore.flclient.LocalFLParameter.ALBERT;
 
+import com.mindspore.flclient.compression.CompressMode;
 import com.mindspore.flclient.model.RunType;
+import mindspore.schema.CompressType;
 
 import java.util.ArrayList;
 import java.util.Arrays;
@@ -603,6 +605,16 @@ public class FLParameter {
         this.batchSize = batchSize;
     }
 
+    public byte[] getDownloadCompressTypes() {
+        byte[] downloadCompressTypes = new byte[CompressMode.COMPRESS_TYPE_MAP.size()];
+        int index = 0;
+        for (byte downloadCompressType : CompressMode.COMPRESS_TYPE_MAP.keySet()) {
+            downloadCompressTypes[index] = downloadCompressType;
+            index += 1;
+        }
+        return downloadCompressTypes;
+    }
+
     public int[][] getInputShape() {
         return inputShape;
     }

mindspore/lite/java/java/fl_client/src/main/java/com/mindspore/flclient/GetModel.java

@@ -18,6 +18,7 @@ package com.mindspore.flclient;
 
 import com.google.flatbuffers.FlatBufferBuilder;
 
+import com.mindspore.flclient.compression.DecodeExecutor;
 import com.mindspore.flclient.model.AlInferBert;
 import com.mindspore.flclient.model.AlTrainBert;
 import com.mindspore.flclient.model.Client;
@@ -27,11 +28,9 @@ import com.mindspore.flclient.model.SessionUtil;
 import com.mindspore.flclient.model.Status;
 
 import com.mindspore.flclient.model.TrainLenet;
-import mindspore.schema.FeatureMap;
-import mindspore.schema.RequestGetModel;
-import mindspore.schema.ResponseCode;
-import mindspore.schema.ResponseGetModel;
+import mindspore.schema.*;
 
+import java.util.List;
 import java.util.ArrayList;
 import java.util.Date;
 import java.util.logging.Logger;
@@ -94,7 +93,8 @@ public class GetModel {
             throw new IllegalArgumentException();
         }
         RequestGetModelBuilder builder = new RequestGetModelBuilder();
-        return builder.iteration(iteration).flName(name).time().build();
+        return builder.iteration(iteration).flName(name).time()
+                .downloadCompressTypesBuilder(flParameter.getDownloadCompressTypes()).build();
     }
 
     private FLClientStatus deprecatedParseResponseAlbert(ResponseGetModel responseDataBuf) {
@@ -226,11 +226,29 @@ public class GetModel {
         return status;
     }
 
+    private List<FeatureMap> parseFeatureMapList(ResponseGetModel responseDataBuf) {
+        List<FeatureMap> featureMaps;
+        byte compressType = responseDataBuf.downloadCompressType();
+        if (responseDataBuf.downloadCompressType() == mindspore.schema.CompressType.NO_COMPRESS) {
+            featureMaps = new ArrayList<>();
+            for (int i = 0; i < responseDataBuf.featureMapLength(); i++) {
+                featureMaps.add(responseDataBuf.featureMap(i));
+            }
+        } else {
+            List<mindspore.schema.CompressFeatureMap> compressFeatureMapList = new ArrayList<>();
+            for (int i = 0; i < responseDataBuf.compressFeatureMapLength(); i++) {
+                compressFeatureMapList.add(responseDataBuf.compressFeatureMap(i));
+            }
+            featureMaps = DecodeExecutor.getInstance().deCompressWeight(compressType, compressFeatureMapList);
+        }
+        return featureMaps;
+    }
+
     private FLClientStatus parseResponseFeatures(ResponseGetModel responseDataBuf) {
         FLClientStatus status;
         Client client = ClientManager.getClient(flParameter.getFlName());
-        int fmCount = responseDataBuf.featureMapLength();
-        if (fmCount <= 0) {
+        List<FeatureMap> featureMapList = parseFeatureMapList(responseDataBuf);
+        if (featureMapList.size() <= 0) {
             LOGGER.severe(Common.addTag("[getModel] the feature size get from server is zero"));
             retCode = ResponseCode.SystemError;
             return FLClientStatus.FAILED;
@@ -239,8 +257,8 @@ public class GetModel {
             LOGGER.info(Common.addTag("[getModel] parseResponseFeatures by " + localFLParameter.getServerMod()));
             ArrayList<FeatureMap> trainFeatureMaps = new ArrayList<FeatureMap>();
             ArrayList<FeatureMap> inferFeatureMaps = new ArrayList<FeatureMap>();
-            for (int i = 0; i < fmCount; i++) {
-                FeatureMap feature = responseDataBuf.featureMap(i);
+            for (int i = 0; i < featureMapList.size(); i++) {
+                FeatureMap feature = featureMapList.get(i);
                 if (feature == null) {
                     LOGGER.severe(Common.addTag("[getModel] the feature returned from server is null"));
                     retCode = ResponseCode.SystemError;
@@ -289,8 +307,8 @@ public class GetModel {
         } else if (localFLParameter.getServerMod().equals(ServerMod.FEDERATED_LEARNING.toString())) {
             LOGGER.info(Common.addTag("[getModel] parseResponseFeatures by " + localFLParameter.getServerMod()));
             ArrayList<FeatureMap> featureMaps = new ArrayList<FeatureMap>();
-            for (int i = 0; i < fmCount; i++) {
-                FeatureMap feature = responseDataBuf.featureMap(i);
+            for (int i = 0; i < featureMapList.size(); i++) {
+                FeatureMap feature = featureMapList.get(i);
                 if (feature == null) {
                     LOGGER.severe(Common.addTag("[getModel] the feature returned from server is null"));
                     retCode = ResponseCode.SystemError;
@@ -365,6 +383,7 @@ public class GetModel {
         private int nameOffset = 0;
         private int iteration = 0;
         private int timeStampOffset = 0;
+        private int downloadCompressTypesOffset = 0;
 
         public RequestGetModelBuilder() {
             builder = new FlatBufferBuilder();
@@ -392,11 +411,23 @@ public class GetModel {
             return this;
         }
 
+        private RequestGetModelBuilder downloadCompressTypesBuilder(byte[] downloadCompressTypes) {
+            if (downloadCompressTypes == null || downloadCompressTypes.length == 0) {
+                LOGGER.severe(Common.addTag("[GetModel] the parameter of <downloadCompressTypes> is null or empty," +
+                        " please check!"));
+                throw new IllegalArgumentException();
+            }
+            this.downloadCompressTypesOffset = RequestGetModel.createDownloadCompressTypesVector(builder,
+                    downloadCompressTypes);
+            return this;
+        }
+
         private byte[] build() {
             RequestGetModel.startRequestGetModel(builder);
             RequestGetModel.addFlName(builder, nameOffset);
             RequestGetModel.addIteration(builder, iteration);
             RequestGetModel.addTimestamp(builder, timeStampOffset);
+            RequestGetModel.addDownloadCompressTypes(builder, downloadCompressTypesOffset);
             int root = RequestGetModel.endRequestGetModel(builder);
             builder.finish(root);
             return builder.sizedByteArray();

mindspore/lite/java/java/fl_client/src/main/java/com/mindspore/flclient/LocalFLParameter.java

@@ -22,6 +22,7 @@ import org.bouncycastle.math.ec.rfc7748.X25519;
 
 import java.util.ArrayList;
 import java.util.List;
+import java.util.Map;
 import java.util.logging.Logger;
 
 /**
@@ -83,6 +84,10 @@ public class LocalFLParameter {
     private MSConfig msConfig = new MSConfig();
     private boolean useSSL = true;
     private float lr = 0.1f;
+    private Map<String, float[]> oldFeatureMap;
+    private byte uploadCompressType = 0;
+    private int seed = 0;
+    private float uploadSparseRatio = 0.08f;
 
 
     private LocalFLParameter() {
@@ -250,4 +255,36 @@ public class LocalFLParameter {
     public void setLr(float lr) {
         this.lr = lr;
     }
+
+    public Map<String, float[]> getOldFeatureMap() {
+        return oldFeatureMap;
+    }
+
+    public void setOldFeatureMap(Map<String, float[]> oldFeatureMap) {
+        this.oldFeatureMap = oldFeatureMap;
+    }
+
+    public byte getUploadCompressType() {
+        return uploadCompressType;
+    }
+
+    public void setUploadCompressType(byte uploadCompressType) {
+        this.uploadCompressType = uploadCompressType;
+    }
+
+    public int getSeed() {
+        return seed;
+    }
+
+    public void setSeed(int seed) {
+        this.seed = seed;
+    }
+
+    public float getUploadSparseRatio() {
+        return uploadSparseRatio;
+    }
+
+    public void setUploadSparseRatio(float uploadSparseRatio) {
+        this.uploadSparseRatio = uploadSparseRatio;
+    }
 }

mindspore/lite/java/java/fl_client/src/main/java/com/mindspore/flclient/SecureProtocol.java

@@ -208,35 +208,34 @@ public class SecureProtocol {
      * @param trainDataSize trainDataSize tne size of train data set.
      * @return the serialized model weights after adding masks.
      */
-    public int[] pwMaskModel(FlatBufferBuilder builder, int trainDataSize, Map<String, float[]> trainedMap) {
+    public Map<String, List<Float>> pwMaskModel(FlatBufferBuilder builder, int trainDataSize, Map<String,
+            float[]> trainedMap) {
+        Map<String, List<Float>> featureMaps = new HashMap<>();
         if (featureMask == null || featureMask.length == 0) {
             LOGGER.severe("[Encrypt] feature mask is null, please check");
-            return new int[0];
+            return new HashMap<>();
         }
         LOGGER.info(String.format("[Encrypt] feature mask size: %s", featureMask.length));
         int featureSize = updateFeatureName.size();
-        int[] featuresMap = new int[featureSize];
         int maskIndex = 0;
         for (int i = 0; i < featureSize; i++) {
             String key = updateFeatureName.get(i);
             float[] data = trainedMap.get(key);
+            List<Float> featureMap = new ArrayList<>();
             LOGGER.info(String.format("[Encrypt] feature name: %s feature size: %s", key, data.length));
             for (int j = 0; j < data.length; j++) {
                 float rawData = data[j];
                 if (maskIndex >= featureMask.length) {
                     LOGGER.severe("[Encrypt] the maskIndex is out of range for array featureMask, please check");
-                    return new int[0];
+                    return new HashMap<>();
                 }
                 float maskData = rawData * trainDataSize + featureMask[maskIndex];
                 maskIndex += 1;
-                data[j] = maskData;
+                featureMap.add(maskData);
             }
-            int featureName = builder.createString(key);
-            int weight = FeatureMap.createDataVector(builder, data);
-            int featureMap = FeatureMap.createFeatureMap(builder, featureName, weight);
-            featuresMap[i] = featureMap;
+            featureMaps.put(key, featureMap);
         }
-        return featuresMap;
+        return featureMaps;
     }
 
     /**
@@ -365,7 +364,9 @@ public class SecureProtocol {
      * @param trainDataSize tne size of train data set.
      * @return the serialized model weights after adding masks.
      */
-    public int[] dpMaskModel(FlatBufferBuilder builder, int trainDataSize, Map<String, float[]> trainedMap) {
+    public Map<String, List<Float>> dpMaskModel(FlatBufferBuilder builder, int trainDataSize,
+                                                Map<String, float[]> trainedMap) {
+        Map<String, List<Float>> featureMaps = new HashMap<>();
         // get feature map
         Map<String, float[]> mapBeforeTrain = modelMap;
         int featureSize = updateFeatureName.size();
@@ -383,7 +384,7 @@ public class SecureProtocol {
                 float rawData = data[j];
                 if (j >= dataBeforeTrain.length) {
                     LOGGER.severe("[Encrypt] the index j is out of range for array dataBeforeTrain, please check");
-                    return new int[0];
+                    return new HashMap<>();
                 }
                 float rawDataBeforeTrain = dataBeforeTrain[j];
                 float updateData = rawData - rawDataBeforeTrain;
@@ -393,23 +394,23 @@ public class SecureProtocol {
         updateL2Norm = Math.sqrt(updateL2Norm);
         if (updateL2Norm == 0) {
             LOGGER.severe(Common.addTag("[Encrypt] updateL2Norm is 0, please check"));
-            return new int[0];
+            return new HashMap<>();
         }
         double clipFactor = Math.min(1.0, dpNormClip / updateL2Norm);
 
         // clip and add noise
-        int[] featuresMap = new int[featureSize];
         for (int i = 0; i < featureSize; i++) {
             String key = updateFeatureName.get(i);
             if (!trainedMap.containsKey(key)) {
                 LOGGER.severe("[Encrypt] the key: " + key + " is not in map, please check!");
-                return new int[0];
+                return new HashMap<>();
             }
             float[] data = trainedMap.get(key);
             float[] data2 = new float[data.length];
+            List<Float> featureMap = new ArrayList<>();
             if (!mapBeforeTrain.containsKey(key)) {
                 LOGGER.severe("[Encrypt] the key: " + key + " is not in mapBeforeTrain, please check!");
-                return new int[0];
+                return new HashMap<>();
             }
             float[] dataBeforeTrain = mapBeforeTrain.get(key);
 
@@ -419,7 +420,7 @@ public class SecureProtocol {
                 float rawData = data[j];
                 if (j >= dataBeforeTrain.length) {
                     LOGGER.severe("[Encrypt] the index j is out of range for array dataBeforeTrain, please check");
-                    return new int[0];
+                    return new HashMap<>();
                 }
                 float rawDataBeforeTrain = dataBeforeTrain[j];
                 float updateData = rawData - rawDataBeforeTrain;
@@ -432,13 +433,11 @@ public class SecureProtocol {
                 updateData += gaussianNoise;
                 data2[j] = rawDataBeforeTrain + updateData;
                 data2[j] = data2[j] * trainDataSize;
+                featureMap.add(data2[j]);
             }
-            int featureName = builder.createString(key);
-            int weight = FeatureMap.createDataVector(builder, data2);
-            int featureMap = FeatureMap.createFeatureMap(builder, featureName, weight);
-            featuresMap[i] = featureMap;
+            featureMaps.put(key, featureMap);
         }
-        return featuresMap;
+        return featureMaps;
     }
 
     /**

mindspore/lite/java/java/fl_client/src/main/java/com/mindspore/flclient/StartFLJob.java

@@ -18,6 +18,7 @@ package com.mindspore.flclient;
 
 import com.google.flatbuffers.FlatBufferBuilder;
 
+import com.mindspore.flclient.compression.DecodeExecutor;
 import com.mindspore.flclient.model.AlInferBert;
 import com.mindspore.flclient.model.AlTrainBert;
 import com.mindspore.flclient.model.Client;
@@ -29,6 +30,7 @@ import com.mindspore.flclient.model.TrainLenet;
 import com.mindspore.flclient.pki.PkiBean;
 import com.mindspore.flclient.pki.PkiUtil;
 
+import mindspore.schema.*;
 import mindspore.schema.FLPlan;
 import mindspore.schema.FeatureMap;
 import mindspore.schema.RequestFLJob;
@@ -38,6 +40,7 @@ import mindspore.schema.ResponseFLJob;
 import java.io.IOException;
 import java.security.cert.Certificate;
 import java.util.ArrayList;
+import java.util.List;
 import java.util.logging.Logger;
 
 import static com.mindspore.flclient.LocalFLParameter.ALBERT;
@@ -119,6 +122,7 @@ public class StartFLJob {
                     .iteration(iteration)
                     .signData(pkiBean.getSignData())
                     .certificateChain(pkiBean.getCertificates())
+                    .downloadCompressTypesBuilder(flParameter.getDownloadCompressTypes())
                     .build();
         }
         return builder.flName(flParameter.getFlName())
@@ -126,6 +130,7 @@ public class StartFLJob {
                 .id(localFLParameter.getFlID())
                 .dataSize(dataSize)
                 .iteration(iteration)
+                .downloadCompressTypesBuilder(flParameter.getDownloadCompressTypes())
                 .build();
     }
 
@@ -151,8 +156,9 @@ public class StartFLJob {
             ArrayList<FeatureMap> albertFeatureMaps = new ArrayList<FeatureMap>();
             ArrayList<FeatureMap> inferFeatureMaps = new ArrayList<FeatureMap>();
             featureSize = 0;
-            for (int i = 0; i < fmCount; i++) {
-                FeatureMap feature = flJob.featureMap(i);
+            List<FeatureMap> featureMapList = parseFeatureMapList(flJob);
+            for (int i = 0; i < featureMapList.size(); i++) {
+                FeatureMap feature = featureMapList.get(i);
                 if (feature == null) {
                     LOGGER.severe(Common.addTag("[startFLJob] the feature returned from server is null"));
                     return FLClientStatus.FAILED;
@@ -233,12 +239,14 @@ public class StartFLJob {
 
     private FLClientStatus deprecatedParseResponseLenet(ResponseFLJob flJob) {
         FLClientStatus status;
-        int fmCount = flJob.featureMapLength();
-        ArrayList<FeatureMap> featureMaps = new ArrayList<FeatureMap>();
         updateFeatureName.clear();
         featureSize = 0;
-        for (int i = 0; i < fmCount; i++) {
-            FeatureMap feature = flJob.featureMap(i);
+        List<FeatureMap> featureMapList = parseFeatureMapList(flJob);
+
+        ArrayList<FeatureMap> featureMaps = new ArrayList<>();
+
+        for (int i = 0; i < featureMapList.size(); i++) {
+            FeatureMap feature = featureMapList.get(i);
             if (feature == null) {
                 LOGGER.severe(Common.addTag("[startFLJob] the feature returned from server is null"));
                 return FLClientStatus.FAILED;
@@ -267,6 +275,24 @@ public class StartFLJob {
         return FLClientStatus.SUCCESS;
     }
 
+    private List<FeatureMap> parseFeatureMapList(ResponseFLJob flJob) {
+        List<FeatureMap> featureMaps;
+        byte compressType = flJob.downloadCompressType();
+        if (flJob.downloadCompressType() == mindspore.schema.CompressType.NO_COMPRESS) {
+            LOGGER.info(Common.addTag("[parseFeatureMapList] create no compress feature map."));
+            featureMaps = new ArrayList<>();
+            for (int i = 0; i < flJob.featureMapLength(); i++) {
+                featureMaps.add(flJob.featureMap(i));
+            }
+        } else {
+            List<CompressFeatureMap> compressFeatureMapList = new ArrayList<>();
+            for (int i = 0; i < flJob.compressFeatureMapLength(); i++) {
+                compressFeatureMapList.add(flJob.compressFeatureMap(i));
+            }
+            featureMaps = DecodeExecutor.getInstance().deCompressWeight(compressType, compressFeatureMapList);
+        }
+        return featureMaps;
+    }
 
     private FLClientStatus hybridFeatures(ResponseFLJob flJob) {
         FLClientStatus status;
@@ -275,8 +301,23 @@ public class StartFLJob {
         ArrayList<FeatureMap> trainFeatureMaps = new ArrayList<FeatureMap>();
         ArrayList<FeatureMap> inferFeatureMaps = new ArrayList<FeatureMap>();
         featureSize = 0;
+        List<FeatureMap> featureMaps;
+        byte compressType = flJob.downloadCompressType();
+        if (compressType == CompressType.NO_COMPRESS) {
+            featureMaps = new ArrayList<>();
+            for (int i = 0; i < fmCount; i++) {
+                featureMaps.add(flJob.featureMap(i));
+            }
+        } else {
+            List<CompressFeatureMap> compressFeatureMapList = new ArrayList<>();
+            for (int i = 0; i < flJob.compressFeatureMapLength(); i++) {
+                compressFeatureMapList.add(flJob.compressFeatureMap(i));
+            }
+            featureMaps = DecodeExecutor.getInstance().deCompressWeight(compressType, compressFeatureMapList);
+            fmCount = featureMaps.size();
+        }
         for (int i = 0; i < fmCount; i++) {
-            FeatureMap feature = flJob.featureMap(i);
+            FeatureMap feature = featureMaps.get(i);
             if (feature == null) {
                 LOGGER.severe(Common.addTag("[startFLJob] the feature returned from server is null"));
                 retCode = ResponseCode.SystemError;
@@ -335,8 +376,23 @@ public class StartFLJob {
         int fmCount = flJob.featureMapLength();
         ArrayList<FeatureMap> featureMaps = new ArrayList<FeatureMap>();
         featureSize = 0;
+        byte compressType = flJob.downloadCompressType();
+        List<FeatureMap> parseFeatureMaps;
+        if (compressType == CompressType.NO_COMPRESS) {
+            parseFeatureMaps = new ArrayList<>();
+            for (int i = 0; i < fmCount; i++) {
+                parseFeatureMaps.add(flJob.featureMap(i));
+            }
+        } else {
+            List<CompressFeatureMap> compressFeatureMapList = new ArrayList<>();
+            for (int i = 0; i < flJob.compressFeatureMapLength(); i++) {
+                compressFeatureMapList.add(flJob.compressFeatureMap(i));
+            }
+            parseFeatureMaps = DecodeExecutor.getInstance().deCompressWeight(compressType, compressFeatureMapList);
+            fmCount = parseFeatureMaps.size();
+        }
         for (int i = 0; i < fmCount; i++) {
-            FeatureMap feature = flJob.featureMap(i);
+            FeatureMap feature = parseFeatureMaps.get(i);
             if (feature == null) {
                 LOGGER.severe(Common.addTag("[startFLJob] the feature returned from server is null"));
                 retCode = ResponseCode.SystemError;
@@ -437,8 +493,8 @@ public class StartFLJob {
 
         switch (responseRetCode) {
             case (ResponseCode.SUCCEED):
-                if (flJob.featureMapLength() <= 0) {
-                    LOGGER.severe(Common.addTag("[startFLJob] the feature size get from server is zero"));
+                if (flJob.downloadCompressType() == CompressType.NO_COMPRESS && flJob.featureMapLength() <= 0) {
+                    LOGGER.warning(Common.addTag("[startFLJob] the feature size get from server is zero"));
                     retCode = ResponseCode.SystemError;
                     return FLClientStatus.FAILED;
                 }
@@ -484,6 +540,7 @@ public class StartFLJob {
         private int equipCertOffset = 0;
         private int equipCACertOffset = 0;
         private int rootCertOffset = 0;
+        private int downloadCompressTypesOffset = 0;
 
         public RequestStartFLJobBuilder() {
             builder = new FlatBufferBuilder();
@@ -598,6 +655,17 @@ public class StartFLJob {
             return this;
         }
 
+        private RequestStartFLJobBuilder downloadCompressTypesBuilder(byte[] downloadCompressTypes) {
+            if (downloadCompressTypes == null || downloadCompressTypes.length == 0) {
+                LOGGER.severe(Common.addTag("[StartFLJob] the parameter of <downloadCompressTypes> is null or empty," +
+                        " please check!"));
+                throw new IllegalArgumentException();
+            }
+            this.downloadCompressTypesOffset = RequestFLJob.createDownloadCompressTypesVector(builder,
+                    downloadCompressTypes);
+            return this;
+        }
+
         /**
          * build protobuffer
          *
@@ -615,6 +683,7 @@ public class StartFLJob {
             RequestFLJob.addEquipCaCert(builder, equipCACertOffset);
             RequestFLJob.addEquipCert(builder, equipCertOffset);
             RequestFLJob.addKeyAttestation(builder, keyAttestationOffset);
+            RequestFLJob.addDownloadCompressTypes(builder, downloadCompressTypesOffset);
             int root = RequestFLJob.endRequestFLJob(builder);
             builder.finish(root);
             return builder.sizedByteArray();

mindspore/lite/java/java/fl_client/src/main/java/com/mindspore/flclient/SyncFLJob.java

@@ -147,6 +147,10 @@ public class SyncFLJob {
             LOGGER.info(Common.addTag("[startFLJob] startFLJob succeed, curIteration: " + flLiteClient.getIteration()));
             updateTryTimePerIter(flLiteClient);
 
+            // Copy weights before training.
+            Map<String, float[]> oldFeatureMap = flLiteClient.getFeatureMap();
+            localFLParameter.setOldFeatureMap(oldFeatureMap);
+
             // create mask
             curStatus = flLiteClient.getFeatureMask();
             if (curStatus == FLClientStatus.RESTART) {

mindspore/lite/java/java/fl_client/src/main/java/com/mindspore/flclient/UpdateModel.java

@@ -26,11 +26,15 @@ import com.mindspore.flclient.model.SessionUtil;
 import com.mindspore.flclient.model.Status;
 import com.mindspore.flclient.model.TrainLenet;
 import com.mindspore.lite.MSTensor;
+import com.mindspore.flclient.compression.EncodeExecutor;
+import com.mindspore.flclient.compression.CompressWeight;
 
 import mindspore.schema.FeatureMap;
+import mindspore.schema.CompressFeatureMap;
 import mindspore.schema.RequestUpdateModel;
 import mindspore.schema.ResponseCode;
 import mindspore.schema.ResponseUpdateModel;
+import static mindspore.schema.CompressType.NO_COMPRESS;
 
 import java.util.ArrayList;
 import java.util.Date;
@@ -208,6 +212,7 @@ public class UpdateModel {
         private RequestUpdateModel requestUM;
         private FlatBufferBuilder builder;
         private int fmOffset = 0;
+        private int compFmOffset = 0;
         private int nameOffset = 0;
         private int idOffset = 0;
         private int timestampOffset = 0;
@@ -215,8 +220,11 @@ public class UpdateModel {
         private int sign = 0;
         private int indexArrayOffset = 0;
         private int iteration = 0;
+        private byte uploadCompressType = 0;
+        private float uploadSparseRate = 0.0f;
         private EncryptLevel encryptLevel = EncryptLevel.NOT_ENCRYPT;
         private float uploadLossOffset = 0.0f;
+        private int nameVecOffset = 0;
 
         private RequestUpdateModelBuilder(EncryptLevel encryptLevel) {
             builder = new FlatBufferBuilder();
@@ -294,34 +302,33 @@ public class UpdateModel {
             } else {
                 trainedMap = getFeatureMap();
             }
+            Map<String, List<Float>> featureMaps = new HashMap<>();
             long startTime;
             long endTime;
             switch (encryptLevel) {
                 case PW_ENCRYPT:
-                    int[] fmOffsetsPW = secureProtocol.pwMaskModel(builder, trainDataSize, trainedMap);
-                    if (fmOffsetsPW == null || fmOffsetsPW.length == 0) {
-                        LOGGER.severe("[Encrypt] the return fmOffsetsPW from <secureProtocol.pwMaskModel> is " +
+                    featureMaps = secureProtocol.pwMaskModel(builder, trainDataSize, trainedMap);
+                    if (featureMaps == null || featureMaps.size() == 0) {
+                        LOGGER.severe("[Encrypt] the return featureMaps from <secureProtocol.pwMaskModel> is " +
                                 "null, please check");
                         throw new IllegalArgumentException();
                     }
-                    this.fmOffset = RequestUpdateModel.createFeatureMapVector(builder, fmOffsetsPW);
                     LOGGER.info(Common.addTag("[Encrypt] pairwise mask model ok!"));
-                    return this;
+                    break;
                 case DP_ENCRYPT:
                     startTime = System.currentTimeMillis();
-                    int[] fmOffsetsDP = secureProtocol.dpMaskModel(builder, trainDataSize, trainedMap);
-                    if (fmOffsetsDP == null || fmOffsetsDP.length == 0) {
-                        LOGGER.severe("[Encrypt] the return fmOffsetsDP from <secureProtocol.dpMaskModel> is " +
+                    featureMaps = secureProtocol.dpMaskModel(builder, trainDataSize, trainedMap);
+                    if (featureMaps == null || featureMaps.size() == 0) {
+                        LOGGER.severe("[Encrypt] the return featureMaps from <secureProtocol.dpMaskModel> is " +
                                 "null, please check");
                         retCode = ResponseCode.RequestError;
                         status = FLClientStatus.FAILED;
                         throw new IllegalArgumentException();
                     }
-                    this.fmOffset = RequestUpdateModel.createFeatureMapVector(builder, fmOffsetsDP);
                     LOGGER.info(Common.addTag("[Encrypt] DP mask model ok!"));
                     endTime = System.currentTimeMillis();
-                    LOGGER.info(Common.addTag("[Encrypt] dp time is: " + (endTime - startTime) + "ms"));
-                    return this;
+                    LOGGER.info(Common.addTag("dp time is " + (endTime - startTime) + "ms"));
+                    break;
                 case SIGNDS:
                     startTime = System.currentTimeMillis();
                     // signds alg return indexArray, and package indexArray into flatbuffer.
@@ -352,31 +359,104 @@ public class UpdateModel {
                     this.fmOffset = RequestUpdateModel.createFeatureMapVector(builder, fmOffsetsSignds);
                     LOGGER.info(Common.addTag("[Encrypt] SignDS mask model ok!"));
                     endTime = System.currentTimeMillis();
-                    LOGGER.info(Common.addTag("[Encrypt] signds time is: " + (endTime - startTime) + "ms"));
+                    LOGGER.info(Common.addTag("signds time is " + (endTime - startTime) + "ms"));
                     return this;
                 case NOT_ENCRYPT:
                 default:
                     startTime = System.currentTimeMillis();
-                    int featureSize = updateFeatureName.size();
-                    int[] fmOffsets = new int[featureSize];
-                    for (int i = 0; i < featureSize; i++) {
-                        String key = updateFeatureName.get(i);
-                        float[] data = trainedMap.get(key);
-                        LOGGER.info(Common.addTag("[updateModel build featuresMap] feature name: " + key + " feature " +
-                                "size: " + data.length));
-                        for (int j = 0; j < data.length; j++) {
-                            data[j] = data[j] * trainDataSize;
+                    for (String name : updateFeatureName) {
+                        float[] data = trainedMap.get(name);
+                        List<Float> featureMap = new ArrayList<>();
+                        for (float datum : data) {
+                            featureMap.add(datum * (float) trainDataSize);
                         }
-                        int featureName = builder.createString(key);
-                        int weight = FeatureMap.createDataVector(builder, data);
-                        int featureMap = FeatureMap.createFeatureMap(builder, featureName, weight);
-                        fmOffsets[i] = featureMap;
+                        featureMaps.put(name, featureMap);
                     }
-                    this.fmOffset = RequestUpdateModel.createFeatureMapVector(builder, fmOffsets);
                     endTime = System.currentTimeMillis();
-                    LOGGER.info(Common.addTag("[Encrypt] not encrypt time is: " + (endTime - startTime) + "ms"));
-                    return this;
+                    LOGGER.info(Common.addTag("not encrypt time is " + (endTime - startTime) + "ms"));
+                    break;
+            }
+            byte uploadCompressType = localFLParameter.getUploadCompressType();
+            if (uploadCompressType != NO_COMPRESS) {
+                startTime = System.currentTimeMillis();
+                this.compFmOffset = buildCompFmOffset(featureMaps, trainDataSize);
+                this.uploadCompressType = localFLParameter.getUploadCompressType();
+                this.uploadSparseRate = localFLParameter.getUploadSparseRatio();
+                this.nameVecOffset = buildNameVecOffset(updateFeatureName);
+                endTime = System.currentTimeMillis();
+                LOGGER.info(Common.addTag("compression time is " + (endTime - startTime) + "ms"));
+                return this;
+            }
+            this.fmOffset = buildFmOffset(featureMaps, updateFeatureName);
+            return this;
+        }
+
+        private int buildCompFmOffset(Map<String, List<Float>> featureMaps, int trainDataSize) {
+            List<CompressWeight> compressWeights = EncodeExecutor.getInstance().encode(featureMaps, trainDataSize);
+            if (compressWeights == null || compressWeights.size() == 0) {
+                LOGGER.severe("[Compression] the return compressWeights from <encodeExecutor.encode> is " +
+                        "null, please check");
+                retCode = ResponseCode.RequestError;
+                status = FLClientStatus.FAILED;
+                throw new IllegalArgumentException();
+            }
+            int compFeatureSize = compressWeights.size();
+            int[] compFmOffsets = new int[compFeatureSize];
+            int index = 0;
+            for (CompressWeight compressWeight : compressWeights) {
+                String weightFullname = compressWeight.getWeightFullname();
+                List<Byte> compressData = compressWeight.getCompressData();
+                float minVal = compressWeight.getMinValue();
+                float maxVal = compressWeight.getMaxValue();
+                byte[] data = new byte[compressData.size()];
+                LOGGER.info(Common.addTag("[updateModel build compressWeight] feature name: "
+                        + weightFullname + ", feature size: " + data.length));
+                for (int j = 0; j < data.length; j++) {
+                    data[j] = compressData.get(j);
+                }
+                int featureName = builder.createString(weightFullname);
+                int weight = CompressFeatureMap.createCompressDataVector(builder, data);
+                int featureMap = CompressFeatureMap.createCompressFeatureMap(builder, featureName, weight,
+                        minVal, maxVal);
+                LOGGER.info(Common.addTag("[Compression]" +
+                        " featureName: " + weightFullname +
+                        ", min_val: " + minVal +
+                        ", max_val: " + maxVal));
+                compFmOffsets[index] = featureMap;
+                index += 1;
+            }
+            return RequestUpdateModel.createCompressFeatureMapVector(builder, compFmOffsets);
+        }
+
+        private int buildNameVecOffset(ArrayList<String> updateFeatureName) {
+            int featureSize = updateFeatureName.size();
+            int[] nameVecOffsets = new int[featureSize];
+            for (int i = 0; i < featureSize; i++) {
+                String key = updateFeatureName.get(i);
+                int featureName = builder.createString(key);
+                nameVecOffsets[i] = featureName;
+            }
+            return RequestUpdateModel.createNameVecVector(builder, nameVecOffsets);
+        }
+
+        private int buildFmOffset(Map<String, List<Float>> featureMaps, ArrayList<String> updateFeatureName) {
+            int featureSize = updateFeatureName.size();
+            int[] fmOffsets = new int[featureSize];
+            for (int i = 0; i < featureSize; i++) {
+                String key = updateFeatureName.get(i);
+                List<Float> featureMap = featureMaps.get(key);
+                float[] data = new float[featureMap.size()];
+                LOGGER.info(Common.addTag("[updateModel build featuresMap] feature name: " + key + " feature " +
+                        "size: " + data.length));
+                for (int j = 0; j < data.length; j++) {
+                    data[j] = featureMap.get(j);
+                }
+                int featureName = builder.createString(key);
+                int weight = FeatureMap.createDataVector(builder, data);
+                int featureMapOff = FeatureMap.createFeatureMap(builder, featureName, weight);
+                fmOffsets[i] = featureMapOff;
             }
+            return RequestUpdateModel.createFeatureMapVector(builder, fmOffsets);
         }
 
         /**
@@ -417,6 +497,10 @@ public class UpdateModel {
             RequestUpdateModel.addFlId(this.builder, idOffset);
             RequestUpdateModel.addTimestamp(builder, this.timestampOffset);
             RequestUpdateModel.addIteration(builder, this.iteration);
+            RequestUpdateModel.addCompressFeatureMap(builder, this.compFmOffset);
+            RequestUpdateModel.addUploadCompressType(builder, this.uploadCompressType);
+            RequestUpdateModel.addUploadSparseRate(builder, this.uploadSparseRate);
+            RequestUpdateModel.addNameVec(builder, this.nameVecOffset);
             RequestUpdateModel.addFeatureMap(builder, this.fmOffset);
             RequestUpdateModel.addSignature(builder, this.signDataOffset);
             RequestUpdateModel.addUploadLoss(builder, this.uploadLossOffset);

mindspore/lite/java/java/fl_client/src/main/java/com/mindspore/flclient/compression/CompressMode.java

@@ -0,0 +1,40 @@
+/*
+ * Copyright (c) Huawei Technologies Co., Ltd. 2019-2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.mindspore.flclient.compression;
+
+import java.util.HashMap;
+import java.util.Map;
+
+import static mindspore.schema.CompressType.NO_COMPRESS;
+import static mindspore.schema.CompressType.QUANT;
+
+/**
+ * The compress mod.
+ *
+ * @since 2021-12-21
+ */
+
+public class CompressMode {
+    // compress type -> num bits
+    public static final Map<Byte, Integer> COMPRESS_TYPE_MAP = new HashMap<>();
+
+    static {
+        COMPRESS_TYPE_MAP.put(NO_COMPRESS, -1);
+        COMPRESS_TYPE_MAP.put(QUANT, 8);
+    }
+
+}

mindspore/lite/java/java/fl_client/src/main/java/com/mindspore/flclient/compression/CompressWeight.java

@@ -0,0 +1,83 @@
+/*
+ * Copyright (c) Huawei Technologies Co., Ltd. 2019-2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.mindspore.flclient.compression;
+
+import java.util.List;
+
+/**
+ * Compress Weight Bean
+ *
+ * @since 2021-12-21
+ */
+public class CompressWeight {
+    private String weightFullname;
+    private List<Byte> compressData;
+    private float minValue;
+    private float maxValue;
+
+    public CompressWeight() {
+    }
+
+    public CompressWeight(String weightFullname, List<Byte> compressData, float minValue, float maxValue) {
+        this.weightFullname = weightFullname;
+        this.compressData = compressData;
+        this.minValue = minValue;
+        this.maxValue = maxValue;
+    }
+
+    public String getWeightFullname() {
+        return weightFullname;
+    }
+
+    public void setWeightFullname(String weightFullname) {
+        this.weightFullname = weightFullname;
+    }
+
+    public List<Byte> getCompressData() {
+        return compressData;
+    }
+
+    public void setCompressData(List<Byte> compressData) {
+        this.compressData = compressData;
+    }
+
+    public float getMinValue() {
+        return minValue;
+    }
+
+    public void setMinValue(float minValue) {
+        this.minValue = minValue;
+    }
+
+    public float getMaxValue() {
+        return maxValue;
+    }
+
+    public void setMaxValue(float maxValue) {
+        this.maxValue = maxValue;
+    }
+
+    @Override
+    public String toString() {
+        return "CompressWeight{" +
+                "weightFullname='" + weightFullname + '\'' +
+                ", compressData=" + compressData +
+                ", minValue=" + minValue +
+                ", maxValue=" + maxValue +
+                '}';
+    }
+}

mindspore/lite/java/java/fl_client/src/main/java/com/mindspore/flclient/compression/DecodeExecutor.java

@@ -0,0 +1,115 @@
+/*
+ * Copyright (c) Huawei Technologies Co., Ltd. 2019-2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.mindspore.flclient.compression;
+
+import com.google.flatbuffers.FlatBufferBuilder;
+
+import com.mindspore.flclient.Common;
+import com.mindspore.flclient.StartFLJob;
+import mindspore.schema.CompressFeatureMap;
+import mindspore.schema.FeatureMap;
+import mindspore.schema.CompressType;
+
+import java.nio.ByteBuffer;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.logging.Logger;
+
+import static mindspore.schema.CompressType.QUANT;
+/**
+ * Compress Executor
+ *
+ * @since 2021-12-21
+ */
+public class DecodeExecutor {
+    private static final Logger LOGGER = Logger.getLogger(DecodeExecutor.class.toString());
+
+    private static volatile DecodeExecutor compressExecutor;
+
+    private DecodeExecutor() {}
+
+    public static DecodeExecutor getInstance() {
+        if (compressExecutor == null) {
+            synchronized (DecodeExecutor.class) {
+                if (compressExecutor == null) {
+                    compressExecutor = new DecodeExecutor();
+                }
+            }
+        }
+        return compressExecutor;
+    }
+
+    public List<FeatureMap> deCompressWeight(byte compressType, List<CompressFeatureMap> compressFeatureMapList) {
+        if (!CompressMode.COMPRESS_TYPE_MAP.containsKey(compressType)) {
+            return new ArrayList<>();
+        }
+        LOGGER.info(Common.addTag("[deCompressWeight] create " + CompressType.name(compressType) + " feature map."));
+        int num_bits = CompressMode.COMPRESS_TYPE_MAP.get(compressType);
+        if (compressType == QUANT) {
+            return deCompressQuantMinMax(compressFeatureMapList, num_bits);
+        }
+        return new ArrayList<>();
+    }
+
+    private List<FeatureMap> deCompressQuantMinMax(List<CompressFeatureMap> compressFeatureMapList, int num_bits) {
+        float temp1 = (float) (Math.pow(2, num_bits) - 1);
+        float temp2 = (float) Math.pow(2, num_bits - 1);
+
+        Map<String, float[]> deCompressFeatureMaps = new HashMap<>();
+        int compressFeatureMapLength = compressFeatureMapList.size();
+        for (int i = 0; i < compressFeatureMapLength; i++) {
+            CompressFeatureMap compressFeatureMap = compressFeatureMapList.get(i);
+            String weightName = compressFeatureMap.weightFullname();
+            int compressDataLength = compressFeatureMap.compressDataLength();
+            List<Byte> compressWeightList = new ArrayList<>();
+            for (int j = 0; j < compressDataLength; j++) {
+                compressWeightList.add(compressFeatureMap.compressData(j));
+            }
+            float minVal = compressFeatureMap.minVal();
+            float maxVal = compressFeatureMap.maxVal();
+            float scale_value = (float) ((maxVal - minVal) / temp1 + 1e-10);
+            float[] params = new float[compressWeightList.size()];
+            for (int j = 0; j < params.length; j++) {
+                float val = (compressWeightList.get(j).intValue() + temp2) * scale_value + minVal;
+                params[j] = val;
+            }
+            deCompressFeatureMaps.put(weightName, params);
+        }
+
+        List<FeatureMap> featureMaps = new ArrayList<>();
+        for (String weightName : deCompressFeatureMaps.keySet()) {
+            FlatBufferBuilder builder = new FlatBufferBuilder(0);
+            int weightFullnameOffset = builder.createString(weightName);
+            float[] data = deCompressFeatureMaps.get(weightName);
+            int dataOffset = FeatureMap.createDataVector(builder, data);
+
+            FeatureMap.startFeatureMap(builder);
+            FeatureMap.addWeightFullname(builder, weightFullnameOffset);
+            FeatureMap.addData(builder, dataOffset);
+
+            int orc = FeatureMap.endFeatureMap(builder);
+            builder.finish(orc);
+            ByteBuffer buf = builder.dataBuffer();
+            FeatureMap featureMap = FeatureMap.getRootAsFeatureMap(buf);
+
+            featureMaps.add(featureMap);
+        }
+        return featureMaps;
+    }
+}

mindspore/lite/java/java/fl_client/src/main/java/com/mindspore/flclient/compression/EncodeExecutor.java

@@ -0,0 +1,167 @@
+/*
+ * Copyright (c) Huawei Technologies Co., Ltd. 2019-2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.mindspore.flclient.compression;
+
+import com.mindspore.flclient.LocalFLParameter;
+import static mindspore.schema.CompressType.DIFF_SPARSE_QUANT;
+
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Queue;
+import java.util.PriorityQueue;
+
+/**
+ * Encode Executor
+ *
+ * @since 2021-12-21
+ */
+public class EncodeExecutor {
+    private final LocalFLParameter localFLParameter = LocalFLParameter.getInstance();
+
+    private static volatile EncodeExecutor encodeExecutor;
+
+    private EncodeExecutor() {}
+
+    public static EncodeExecutor getInstance() {
+        if (encodeExecutor == null) {
+            synchronized (EncodeExecutor.class) {
+                if (encodeExecutor == null) {
+                    encodeExecutor = new EncodeExecutor();
+                }
+            }
+        }
+        return encodeExecutor;
+    }
+
+    private static final int multiplier = 2147483647;
+    private static final double increment = 4294967294.0;
+    private static final int modulo = 48271;
+
+    private List<Integer> constructMaskArray(int paramNum) {
+        int seed = localFLParameter.getSeed();
+        float uploadSparseRatio = localFLParameter.getUploadSparseRatio();
+
+        List<Integer> maskArray = new ArrayList<>();
+
+        int retain_num = (int) ((float) (paramNum) * uploadSparseRatio);
+        for (int i = 0; i < retain_num; ++i) {
+            maskArray.add(1);
+        }
+        for (int i = retain_num; i < paramNum; ++i) {
+            maskArray.add(0);
+        }
+
+        seed = ((seed + multiplier) * modulo) % multiplier;
+        for (int i = 0; i < paramNum; ++i) {
+            // generate random number in (0, 1)
+            double rand = (double)(seed) / increment + 0.5;
+            // update seed
+            seed = (seed * modulo) % multiplier;
+
+            int j = (int)(rand * (double)(paramNum - i)) + i;
+            int temp = maskArray.get(i);
+            maskArray.set(i, maskArray.get(j));
+            maskArray.set(j, temp);
+        }
+        return maskArray;
+    }
+
+    public List<CompressWeight> enDiffSparseQuant(Map<String, List<Float>> featureMaps, int numBits,
+                                                  int trainDataSize) {
+        List<CompressWeight> compressWeights = new ArrayList<>();
+
+        // difference encode
+        Map<String, float[]> oldFeatureMap = localFLParameter.getOldFeatureMap();
+        Map<String, List<Float>> diffFeatureMaps = new HashMap<>();
+        for (String featureMapName : featureMaps.keySet()) {
+            List<Float> diffs = new ArrayList<>();
+            List<Float> featureMap = featureMaps.get(featureMapName);
+            float[] dataBeforeTrain = oldFeatureMap.get(featureMapName);
+            int length = dataBeforeTrain.length;
+            for (int i = 0; i < length; ++i) {
+                float diff = featureMap.get(i) - dataBeforeTrain[i] * (float) trainDataSize;
+                diffs.add(diff);
+            }
+            diffFeatureMaps.put(featureMapName, diffs);
+        }
+
+        // sparse encode
+        int paramNum = 0;
+        for (String featureMapName : diffFeatureMaps.keySet()) {
+            int weightSize = diffFeatureMaps.get(featureMapName).size();
+            paramNum += weightSize;
+        }
+        List<Integer> maskArray = constructMaskArray(paramNum);
+
+        Map<String, List<Float>> sparseFeatureMaps = new HashMap<>();
+        int index = 0;
+        for (String featureMapName : diffFeatureMaps.keySet()) {
+            List<Float> sparseFeatureMap = new ArrayList<>();
+            List<Float> Weight = diffFeatureMaps.get(featureMapName);
+            for (Float dataValue : Weight) {
+                if (maskArray.get(index) == 1) {
+                    sparseFeatureMap.add(dataValue);
+                }
+                index += 1;
+            }
+            sparseFeatureMaps.put(featureMapName, sparseFeatureMap);
+        }
+
+        // quant encode
+        float temp1 = (float) (1 << numBits) - 1.0f;
+        float temp2 = (float) (1 << (numBits - 1));
+        for (String featureMapName : sparseFeatureMaps.keySet()) {
+            CompressWeight compressWeight = new CompressWeight();
+            compressWeight.setWeightFullname(featureMapName);
+
+            List<Float> sparseFeatureMap = sparseFeatureMaps.get(featureMapName);
+
+            // get min and max value
+            Float minVal = Float.MAX_VALUE;
+            float maxVal = -minVal;
+            for (Float value : sparseFeatureMap) {
+                if (value < minVal) {
+                    minVal = value;
+                }
+                if (value > maxVal) {
+                    maxVal = value;
+                }
+            }
+            compressWeight.setMinValue(minVal);
+            compressWeight.setMaxValue(maxVal);
+            float scale_value = (maxVal - minVal) / temp1 + 1e-10f;
+            List<Byte> compressData = new ArrayList<>();
+            for (Float aFloat : sparseFeatureMap) {
+                compressData.add((byte) (Math.round((aFloat - minVal) / scale_value - temp2)));
+            }
+            compressWeight.setCompressData(compressData);
+            compressWeights.add(compressWeight);
+        }
+
+        return compressWeights;
+    }
+
+    public List<CompressWeight> encode(Map<String, List<Float>> featureMaps, int trainDataSize) {
+        byte uploadCompressType = localFLParameter.getUploadCompressType();
+        if (uploadCompressType == DIFF_SPARSE_QUANT) {
+            return enDiffSparseQuant(featureMaps, 8, trainDataSize);
+        }
+        throw new IllegalArgumentException();
+    }
+}

mindspore/python/mindspore/parallel/_ps_context.py

@@ -15,8 +15,9 @@
 """Context for parameter server training mode"""
 
 import os
-from mindspore._checkparam import Validator
+from mindspore._checkparam import Validator, Rel
 from mindspore._c_expression import PSContext
+from mindspore import log as logger
 
 _ps_context = None
 
@@ -79,6 +80,9 @@ _set_ps_context_func_map = {
     "sign_global_lr": ps_context().set_sign_global_lr,
     "sign_dim_out": ps_context().set_sign_dim_out,
     "checkpoint_dir": ps_context().set_checkpoint_dir,
+    "upload_compress_type": ps_context().set_upload_compress_type,
+    "upload_sparse_rate": ps_context().set_upload_sparse_rate,
+    "download_compress_type": ps_context().set_download_compress_type,
 }
 
 _get_ps_context_func_map = {
@@ -126,7 +130,10 @@ _get_ps_context_func_map = {
     "sign_thr_ratio": ps_context().sign_thr_ratio,
     "sign_global_lr": ps_context().sign_global_lr,
     "sign_dim_out": ps_context().sign_dim_out,
-    "checkpoint_dir": ps_context().checkpoint_dir
+    "checkpoint_dir": ps_context().checkpoint_dir,
+    "upload_compress_type": ps_context().upload_compress_type,
+    "upload_sparse_rate": ps_context().upload_sparse_rate,
+    "download_compress_type": ps_context().download_compress_type,
 }
 
 _check_positive_int_keys = ["server_num", "scheduler_port", "fl_server_port",
@@ -140,6 +147,15 @@ _check_positive_float_keys = ["update_model_ratio", "client_learning_rate"]
 
 _check_port_keys = ["scheduler_port", "fl_server_port"]
 
+_check_string_keys = {
+    "upload_compress_type": ["NO_COMPRESS", "DIFF_SPARSE_QUANT"],
+    "download_compress_type": ["NO_COMPRESS", "QUANT"],
+}
+
+_check_float_range_keys = {
+    "upload_sparse_rate": {"lower_limit": 0.0, "upper_limit": 1.0, "rel": Rel.INC_RIGHT},
+}
+
 def _get_ps_mode_rank():
     ps_rank = ps_context().ps_rank_id()
     if ps_rank == -1:
@@ -183,6 +199,7 @@ def _set_ps_context(**kwargs):
     Examples:
         >>> context.set_ps_context(enable_ps=True, enable_ssl=True, client_password='123456', server_password='123456')
     """
+    kwargs = _check_conflict_value(kwargs)
     for key, value in kwargs.items():
         if key not in _set_ps_context_func_map:
             raise ValueError("Set PS context keyword %s is not recognized!" % key)
@@ -287,6 +304,31 @@ def _check_value(key, value):
     if key in _check_positive_float_keys:
         Validator.check_positive_float(value, key)
 
+    if key in _check_string_keys:
+        try:
+            string_keys = _check_string_keys[key]
+            Validator.check_string(value, string_keys)
+        except KeyError:
+            pass
+
+    if key in _check_float_range_keys:
+        try:
+            range_keys = _check_float_range_keys[key]
+            Validator.check_float_range(value, **range_keys)
+        except KeyError:
+            pass
+
     if key in _check_port_keys:
         if value < 1 or value > 65535:
             raise ValueError("The range of %s must be 1 to 65535, but got %d." % (key, value))
+
+
+def _check_conflict_value(kwargs):
+    if "upload_compress_type" in kwargs and " encrypt_type" in kwargs:
+        if kwargs["upload_compress_type"] != "NO_COMPRESS" and kwargs["encrypt_type"] in ("SIGNDS", "PW_ENCRYPT"):
+            logger.warning("The '{}' and '{}' are conflicted, and in '{}' mode the"
+                           " 'upload_compress_type' will be 'NO_COMPRESS'".format(kwargs["encrypt_type"],
+                                                                                  kwargs["upload_compress_type"],
+                                                                                  kwargs["encrypt_type"]))
+            kwargs["upload_compress_type"] = "NO_COMPRESS"
+    return kwargs

mindspore/schema/fl_job.fbs

@@ -47,6 +47,16 @@ table FeatureMap{
   weight_fullname:string;
   data:[float];
 }
+
+enum CompressType:byte {NO_COMPRESS = 0, DIFF_SPARSE_QUANT = 1, QUANT = 2}
+
+table CompressFeatureMap{
+  weight_fullname:string;
+  compress_data:[int8];
+  min_val:float;
+  max_val:float;
+}
+
 table RequestFLJob{
   fl_name:string;
   fl_id:string;
@@ -58,6 +68,7 @@ table RequestFLJob{
   equip_cert:string;
   equip_ca_cert:string;
   root_cert:string;
+  download_compress_types:[CompressType];
 }
 table ResponseFLJob {
   retcode:int;
@@ -68,6 +79,10 @@ table ResponseFLJob {
   fl_plan_config:FLPlan;
   feature_map:[FeatureMap];
   timestamp:string;
+  upload_compress_type:CompressType;
+  upload_sparse_rate:float;
+  download_compress_type:CompressType;
+  compress_feature_map:[CompressFeatureMap];
 }
 
 table FLPlan {
@@ -94,6 +109,10 @@ table RequestUpdateModel{
   upload_loss:float;
   sign:int;
   index_array:[int];
+  compress_feature_map:[CompressFeatureMap];
+  upload_compress_type:CompressType;
+  upload_sparse_rate:float;
+  name_vec:[string];
 }
 
 table ResponseUpdateModel{
@@ -132,6 +151,7 @@ table RequestGetModel{
   fl_name:string;
   iteration:int;
   timestamp:string;
+  download_compress_types:[CompressType];
 }
 table ResponseGetModel{
   retcode:int;
@@ -139,6 +159,8 @@ table ResponseGetModel{
   iteration:int;
   feature_map:[FeatureMap];
   timestamp:string;
+  download_compress_type:CompressType;
+  compress_feature_map:[CompressFeatureMap];
 }
 
 table RequestAsyncGetModel{