Merge pull request #304 from primihub/develop

1.6.2

.bazelrc

 # Linux
-build:linux --cxxopt=-std=c++17
-build:linux --host_cxxopt=-std=c++17
-build:linux --copt=-w
-build:linux --copt=-DENABLE_SSE
-build:linux --linkopt=-lstdc++fs
-build:linux --define microsoft-apsi=false
+build:linux_x86_64 --cxxopt=-std=c++17
+build:linux_x86_64 --host_cxxopt=-std=c++17
+build:linux_x86_64 --copt=-w
+build:linux_x86_64 --linkopt=-lstdc++fs
+build:linux_x86_64 --copt=-DENABLE_SSE
+build:linux_x86_64 --define cpu=amd64
+build:linux_x86_64 --define cpu_arch=x86_64
+build:linux_x86_64 --define microsoft-apsi=true
+build:linux_x86_64 --define enable_mysql_driver=true
+
+build:linux_aarch64 --cxxopt=-std=c++17
+build:linux_aarch64 --host_cxxopt=-std=c++17
+build:linux_aarch64 --copt=-w
+build:linux_aarch64 --linkopt=-lstdc++fs
+build:linux_aarch64 --define cpu=arm64
+build:linux_aarch64 --define cpu_arch=aarch64
+build:linux_aarch64 --define microsoft-apsi=true
+build:linux_aarch64 --define enable_mysql_driver=true
 
 #build:linux --strip=never
 #build:linux --copt -fno-sanitize-recover=all
@@ -47,12 +59,16 @@ build:darwin_x86_64 --macos_minimum_os=10.16
 build:darwin_x86_64 --cpu=darwin_x86_64
 build:darwin_x86_64 --copt=-DENABLE_SSE
 build:darwin_x86_64 --define macos-build=true
+build:darwin_x86_64 --define cpu_arch=darwin_x86_64
+build:darwin_x86_64 --define enable_mysql_driver=false
 
 # MacOS Big Sur with Apple Silicon M1
 build:darwin_arm64 --apple_platform_type=macos
 build:darwin_arm64 --macos_minimum_os=10.16
 build:darwin_arm64 --cpu=darwin_arm64
 build:darwin_arm64 --define macos-build=true
+build:darwin_arm64 --define cpu_arch=darwin_arm64
+build:darwin_arm64 --define enable_mysql_driver=false
 
 # MacOS Monterey with Apple M1
 build:darwin --apple_platform_type=macos

.github/workflows/main.yml

@@ -63,7 +63,7 @@ jobs:
     - name: bazel test
       run: |
         ./pre_build.sh
-        bazel build --config=linux logistic_test maxpool_test falcon_lenet_test common_test network_test
+        bazel build --config=linux_x86_64 logistic_test maxpool_test falcon_lenet_test common_test network_test
         ./bazel-bin/logistic_test
         ./bazel-bin/maxpool_test
         ./bazel-bin/falcon_lenet_test
@@ -143,7 +143,7 @@ jobs:
     - name: bazel build
       run: |
         # cc_binary
-        bazel build --config=linux :node :cli :opt_paillier_c2py :linkcontext 
+        bazel build --config=linux_x86_64 :node :cli :opt_paillier_c2py :linkcontext
 
 
   build-on-mac_x86_64:

BUILD.bazel

@@ -36,6 +36,25 @@ config_setting(
    values = {"define": "microsoft-apsi=true"},
 )
 
+config_setting(
+    name = "aarch64",
+    values = {"define": "cpu_arch=aarch64"},
+)
+
+config_setting(
+    name = "x86_64",
+    values = {"define": "cpu_arch=x86_64"},
+)
+config_setting(
+    name = "darwin_x86_64",
+    values = {"define": "cpu_arch=darwin_x86_64"},
+)
+
+config_setting(
+    name = "enable_mysql_driver",
+    values = {"define": "enable_mysql_driver=true"},
+)
+
 DEFAULT_LINK_OPTS = [
     "-pthread",
     "-ldl",
@@ -66,10 +85,12 @@ C_OPT = [
     "-Wall",
     "-ggdb",
     "-rdynamic",
-    "-maes",
-    "-mpclmul",
     "-Wno-reserved-user-defined-literal",
-]
+] + select({
+    ":x86_64": ["-maes", "-mpclmul"],
+    ":aarch64": [],
+    "//conditions:default": [],
+})
 
 ## start of primihub
 load("@com_github_grpc_grpc//bazel:grpc_build_system.bzl", "grpc_proto_library")
@@ -91,7 +112,6 @@ alias(
     actual = "@soralog//:soralog",
 )
 
-
 # proto
 grpc_proto_library(
     name = "route_guide",
@@ -130,27 +150,21 @@ grpc_proto_library(
 )
 
 cc_library(
-    name = "common_lib",
-    srcs = glob([
-            "src/primihub/common/defines.cc",
-            "src/primihub/common/clp.cc",
-            "src/primihub/common/config/config.cc",
-            "src/primihub/common/type/type.cc",
-            "src/primihub/common/type/fixed_point.cc",
-    ]),
-    hdrs = glob([
-            "src/primihub/common/defines.h",
-            "src/primihub/common/finally.h",
-            "src/primihub/common/clp.h",
-            "src/primihub/common/config/config.h",
-            "src/primihub/common/type/type.h",
-            "src/primihub/common/type/fixed_point.h",
-            "src/primihub/common/type/matrix.h",
-            "src/primihub/common/type/matrix_view.h",
-            "src/primihub/common/eventbus/eventbus.hpp",
-            "src/primihub/common/eventbus/function_traits.hpp",
+    name = "server_config",
+    hdrs = [
+        "src/primihub/node/server_config.h",
+    ],
+    srcs = [
+        "src/primihub/node/server_config.cc",
+    ],
+    deps = [
+        ":config_lib",
+        "@com_github_jbeder_yaml_cpp//:yaml-cpp",
+    ],
+)
 
-    ]),
+cc_library(
+    name = "microsoft_gsl",
     textual_hdrs = [
         "src/primihub/common/gsl/span",
         "src/primihub/common/gsl/gsl_assert",
@@ -166,11 +180,114 @@ cc_library(
         "--std=c++17",
     ],
     linkopts = LINK_OPTS,
+)
+
+cc_library(
+    name = "common_defination",
+    hdrs = [
+        "src/primihub/common/common.h",
+    ],
+)
+
+cc_library(
+    name = "config_lib",
+    hdrs = [
+        "src/primihub/common/config/config.h",
+    ],
+    srcs = [
+        "src/primihub/common/config/config.cc",
+    ],
+    deps = [
+        ":common_defination",
+        "@com_github_jbeder_yaml_cpp//:yaml-cpp",
+        "@com_github_glog_glog//:glog",
+    ]
+)
+
+cc_library(
+    name = "clp_lib",
+    hdrs = [
+        "src/primihub/common/clp.h",
+    ],
+    srcs = [
+        "src/primihub/common/clp.cc",
+    ],
+    deps = [
+        ":common_defination",
+        ":microsoft_gsl",
+    ],
+)
+
+cc_library(
+    name = "eventbus_lib",
+    hdrs = [
+        "src/primihub/common/eventbus/eventbus.hpp",
+        "src/primihub/common/eventbus/function_traits.hpp",
+    ],
+)
+cc_library(
+    name = "data_type_defination",
+    hdrs = [
+        "src/primihub/common/type/type.h",
+        "src/primihub/common/type/fixed_point.h",
+        "src/primihub/common/type/matrix.h",
+        "src/primihub/common/type/matrix_view.h",
+    ],
+    srcs = [
+        "src/primihub/common/type/type.cc",
+        "src/primihub/common/type/fixed_point.cc",
+    ],
+    copts = C_OPT + [
+        "--std=c++17",
+    ],
     linkstatic = False,
     deps = [
         ":eigen",
         "@boost//:multiprecision",
+        ":microsoft_gsl",
+        ":common_util",
+    ],
+)
+
+cc_library(
+    name = "common_util",
+    hdrs = [
+        "src/primihub/common/defines.h",
+    ],
+    srcs = [
+        "src/primihub/common/defines.cc",
+    ],
+    linkopts = LINK_OPTS,
+    linkstatic = False,
+    deps = [
+        ":microsoft_gsl",
+        ":common_defination",
+    ],
+)
+cc_library (
+    name = "finally_tool",
+    hdrs = [
+        "src/primihub/common/finally.h",
+    ],
+)
+
+cc_library(
+    name = "common_lib",
+    copts = C_OPT + [
+        "--std=c++17",
+    ],
+    linkopts = LINK_OPTS,
+    linkstatic = False,
+    deps = [
+        ":common_defination",
+        ":config_lib",
+        ":common_util",
+        ":clp_lib",
+        ":eventbus_lib",
+        ":finally_tool",
+        ":data_type_defination",
         "@com_github_jbeder_yaml_cpp//:yaml-cpp",
+        "@com_github_glog_glog//:glog",
     ],
 )
 
@@ -229,7 +346,7 @@ cc_library(
     deps = [
         "@com_github_glog_glog//:glog",
         "@com_github_grpc_grpc//:grpc++",
-        ":common_lib",
+        ":config_lib",
         ":worker_proto",
     ],
 )
@@ -389,12 +506,12 @@ cc_library(
         "-I src/primihub/protocol/cryptflow2/OT/",
         "-I src/primihub/protocol/cryptflow2/utils/",
         "-D SCI_OT",
-    "-maes",
-    "-msse4.1",
-    "-mavx2",
         "-pthread",
-    "-mrdseed",
-  ],
+    ] + select({
+        ":x86_64": ["-maes", "-msse4.1", "-mavx2", "-mrdseed",],
+        ":aarch64": [],
+        "//conditions:default": [],
+    }),
     linkopts = ["-fopenmp"],
     deps = [
         "@com_microsoft_seal//:seal",
@@ -421,11 +538,14 @@ cc_library(
 	    # TODO: Consider to remove -I flag.
         "-I src/primihub/protocol/falcon-public/",
         "-I src/primihub/protocol/falcon-public/util/",
-            "-mpclmul",
-            "-maes",
         "-fpic",
 	    "-Wno-narrowing",
-    ],
+    ] + select({
+        ":x86_64": ["-maes", "-mpclmul"],
+        ":darwin_x86_64": ["-maes", "-mpclmul"],
+        ":aarch64": [],
+        "//conditions:default": [],
+    }),
     linkstatic = False,
     deps = [
 	    ":eigen",
@@ -476,11 +596,11 @@ cc_library(
         "src/primihub/service/dataset/storage_backend.h",
 	    "src/primihub/util/cpu_check.h"
     ]),
-    copts = C_OPT + [
-      	   "-maes",
-      	   "-mavx2",
-      	   "-mrdseed",
-    ],
+    copts = C_OPT + select({
+        ":x86_64": ["-maes", "-mrdseed", "-mavx2"],
+        ":aarch64": [],
+        "//conditions:default": [],
+    }),
     linkopts = LINK_OPTS,
     linkstatic = False,
     deps = [
@@ -522,11 +642,11 @@ cc_library(
             "src/primihub/operator/aby3_operator.h",
             "src/primihub/algorithm/missing_val_processing.h",
     ]),
-    copts = C_OPT + [
-      "-maes",
-      "-msse4.1",
-      "-mrdseed",
-    ],
+    copts = C_OPT + select({
+        ":x86_64": ["-maes", "-mrdseed", "-msse4.1"],
+        ":aarch64": [],
+        "//conditions:default": [],
+    }),
     linkopts = LINK_OPTS,
     linkstatic = False,
     deps = [
@@ -608,8 +728,10 @@ PIR_LIB_DEPS = select({
 
 
 LINUX_X86_BUILD_LIBS = select({
-    ":macos-build": [] ,
-    "//conditions:default": [":cryptflow2_algorithm_lib", "@osu_libpsi//:libpsi"],
+    ":aarch64": [],
+    ":x86_64":  [":cryptflow2_algorithm_lib", "@osu_libpsi//:libpsi"],
+    ":darwin_x86_64": [],
+    "//conditions:default": [],
 })
 
 TASK_LIB_DEPS = DEFAULT_TASK_LIB_DEPS + PIR_LIB_DEPS + LINUX_X86_BUILD_LIBS
@@ -741,6 +863,7 @@ cc_library(
     deps = TASK_LIB_DEPS + [
         ":communication_lib",
         ":endian_util",
+        ":server_config",
     ],
 )
 
@@ -748,12 +871,10 @@ cc_library(
     name = "node_lib",
     srcs = glob([
         "src/primihub/node/worker/worker.cc",
-
         "src/primihub/algorithm/dataload.cpp",
     ]),
     hdrs = glob([
         "src/primihub/node/worker/worker.h",
-
     ]),
     copts = C_OPT,
     linkopts = LINK_OPTS,
@@ -909,7 +1030,12 @@ cc_library(
         "src/primihub/data_store/csv/csv_driver.cc",
         # "src/primihub/data_store/hdfs/hdfs_driver.cc",
         "src/primihub/data_store/sqlite/sqlite_driver.cc",
+    ] + select({
+        "enable_mysql_driver": [
+            "src/primihub/data_store/mysql/mysql_driver.cc",
         ],
+        "//conditions:default": []
+    }),
     hdrs = [
         "src/primihub/data_store/factory.h",
         "src/primihub/data_store/dataset.h",
@@ -918,8 +1044,21 @@ cc_library(
         "src/primihub/data_store/csv/csv_driver.h",
         #"src/primihub/data_store/hdfs/hdfs_driver.h",
         "src/primihub/data_store/sqlite/sqlite_driver.h",
+    ] + select({
+        "enable_mysql_driver": [
+            "src/primihub/data_store/mysql/mysql_driver.h",
         ],
-    linkopts = LINK_OPTS,
+        "//conditions:default": []
+    }),
+
+    defines = select({
+        "enable_mysql_driver": ["ENABLE_MYSQL_DRIVER"],
+        "//conditions:default": []
+    }),
+    linkopts = LINK_OPTS + select({
+        "enable_mysql_driver": ["-lmysqlclient"],
+        "//conditions:default": []
+    }),
     deps = [
         "@arrow",
         "@com_github_glog_glog//:glog",
@@ -930,26 +1069,23 @@ cc_library(
         ":util_lib",
         "@nlohmann_json",
         "@com_github_sqlite_wrapper//:sqlite_wrapper",
+        "@com_github_jbeder_yaml_cpp//:yaml-cpp",
     ],
     linkstatic = True,
     visibility = ["//visibility:public"],
 )
 
-
 cc_library(
     name = "new_data_store_lib",
     srcs = [
             "src/primihub/data_store/driver.cc",
             "src/primihub/data_store/csv/csv_driver.cc",
             # "src/primihub/data_store/hdfs/hdfs_driver.cc",
-
-
     ],
     hdrs = [
             "src/primihub/data_store/factory.h",
             "src/primihub/data_store/dataset.h",
             "src/primihub/data_store/driver.h",
-
             "src/primihub/data_store/csv/csv_driver.h",
             "src/primihub/data_store/hdfs/hdfs_driver.h",
 
@@ -963,8 +1099,6 @@ cc_library(
     visibility = ["//visibility:public"],
 )
 
-
-
 cc_library(
     name = "p2p_lib",
     srcs = [
@@ -1042,7 +1176,8 @@ cc_library(
         ":p2p_lib",
         ":data_store_lib",
         ":util_lib",
-        "service_base",
+        ":service_base",
+        ":server_config",
     ],
     linkstatic = True,
     visibility = ["//visibility:public"],
@@ -1077,7 +1212,6 @@ cc_library(
     visibility = ["//visibility:public"],
 )
 
-
 cc_library(
     name = "nodelet",
     srcs = [
@@ -1213,10 +1347,10 @@ cc_binary(
         "src/primihub/node/node.h",
         "src/primihub/node/ds.cc",
         "src/primihub/node/ds.h",
+        "src/primihub/node/main.cc",
     ]),
     copts = C_OPT,
-    includes = [
-    ],
+    includes = [],
     linkstatic = True,
     linkopts = LINK_OPTS ,
     deps = [
@@ -1235,7 +1369,7 @@ cc_binary(
         ":nodelet",
         ":algorithm_lib",
         ":util_lib",
-
+        ":server_config",
     ],
 )
 
@@ -1264,9 +1398,34 @@ cc_binary(
         ":util_lib",
         "@openssl",
         "@com_github_stduuid//:stduuid_lib",
+        ":communication_lib",
     ],
 )
 
+cc_binary(
+    name = "reg_cli_test",
+    srcs = [
+        "src/primihub/cli/reg_cli.cc",
+        "src/primihub/cli/reg_cli.h",
+    ],
+    copts = C_OPT,
+    includes = [
+    ],
+    linkstatic = True,
+    linkopts = LINK_OPTS,
+    deps = [
+        "@com_github_grpc_grpc//:grpc++",
+        "@com_google_absl//absl/base",
+        "@com_google_absl//absl/flags:flag",
+        "@com_google_absl//absl/flags:parse",
+        "@com_google_absl//absl/memory",
+        "@com_github_glog_glog//:glog",
+        ":worker_proto",
+        ":service_proto",
+        "@openssl",
+        "@com_github_stduuid//:stduuid_lib",
+    ],
+)
 ## end of primihub
 
 ## unit tests

Dockerfile

@@ -12,15 +12,28 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+FROM ubuntu:20.04 as builder
 
-FROM primihub/primihub-node:build as builder
+ENV LANG C.UTF-8
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Install dependencies
+RUN  apt update \
+  && apt install -y python3 python3-dev gcc-8 g++-8 python-dev libgmp-dev cmake libmysqlclient-dev\
+  && apt install -y automake ca-certificates git libtool m4 patch pkg-config unzip make wget curl zip ninja-build npm \
+  && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 800 --slave /usr/bin/g++ g++ /usr/bin/g++-8 \
+  && rm -rf /var/lib/apt/lists/*
+
+# install  bazelisk
+RUN npm install -g @bazel/bazelisk
 
 WORKDIR /src
 ADD . /src
 
 # Bazel build primihub-node & primihub-cli & paillier shared library
 RUN bash pre_build.sh \
-  && bazel build --config=linux --define cpu=amd64 --define microsoft-apsi=true :node :cli :opt_paillier_c2py :linkcontext
+  && ARCH=`arch` \
+  && bazel build --config=linux_$ARCH :node :cli :opt_paillier_c2py :linkcontext
 
 FROM ubuntu:20.04 as runner
 
@@ -53,10 +66,9 @@ RUN mkdir -p src/primihub/protos data log
 COPY --from=builder /src/python ./python
 COPY --from=builder /src/src/primihub/protos/ ./src/primihub/protos/
 
-# Copy opt_paillier_c2py.so to /app/python, this enable setup.py find it.
-RUN cp $TARGET_PATH/opt_paillier_c2py.so /app/python/
-# Copy linkcontext.so to /app/python, this enable setup.py find it.
-RUN cp $TARGET_PATH/linkcontext.so /app/python/
+# Copy opt_paillier_c2py.so linkcontext.so to /app/python, this enable setup.py find it.
+RUN cp $TARGET_PATH/opt_paillier_c2py.so /app/python/ \
+  && cp $TARGET_PATH/linkcontext.so /app/python/
 
 # The setup.py will copy opt_paillier_c2py.so to python library path.
 WORKDIR /app/python
@@ -64,8 +76,8 @@ RUN python3 -m pip install --upgrade pip \
   && python3 -m pip install -r requirements.txt \
   && python3 setup.py install
 
-RUN rm -rf /app/python/opt_paillier_c2py.so
-RUN rm -rf /app/python/linkcontext.so
+RUN rm -rf /app/python/opt_paillier_c2py.so \
+  && rm -rf /app/python/linkcontext.so
 WORKDIR /app
 
 # gRPC server port

Dockerfile.build

-# Copyright 2022 Primihub
-#
-# 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
-#
-#      https://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.
-
-
-FROM ubuntu:20.04
-
-ENV LANG C.UTF-8
-ENV DEBIAN_FRONTEND=noninteractive
-
-# Install dependencies
-RUN  apt update \
-  && apt install -y python3 python3-dev gcc-8 g++-8 python-dev libgmp-dev cmake \
-  && apt install -y automake ca-certificates git libtool m4 patch pkg-config unzip make wget curl zip ninja-build npm \
-  && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 800 --slave /usr/bin/g++ g++ /usr/bin/g++-8 \
-  && rm -rf /var/lib/apt/lists/*
-
-# install  bazelisk
-RUN npm install -g @bazel/bazelisk
-
-# Install keyword PIR dependencies
-WORKDIR /opt
-RUN wget https://github.com/zeromq/libzmq/archive/refs/tags/v4.3.4.tar.gz \
-  && tar -zxf v4.3.4.tar.gz && mkdir libzmq-4.3.4/build && cd libzmq-4.3.4/build \
-  && cmake .. && make -j 8 && make install
-
-RUN wget https://github.com/zeromq/cppzmq/archive/refs/tags/v4.9.0.tar.gz \
-  && tar -zxf v4.9.0.tar.gz && mkdir cppzmq-4.9.0/build && cd cppzmq-4.9.0/build \
-  && cmake .. && make -j 8 && make install
-
-RUN wget https://github.com/google/flatbuffers/archive/refs/tags/v2.0.0.tar.gz \
-  && tar -zxf v2.0.0.tar.gz && mkdir flatbuffers-2.0.0/build && cd flatbuffers-2.0.0/build \
-  && cmake .. && make -j 8 && make install
-
-RUN wget https://sourceforge.net/projects/tclap/files/tclap-1.2.5.tar.gz \
-  && tar -zxvf tclap-1.2.5.tar.gz && cd tclap-1.2.5 && ./configure \
-  && make -j 8 && make install
\ No newline at end of file

Dockerfile.local

@@ -6,7 +6,7 @@ ENV DEBIAN_FRONTEND=noninteractive
 ENV PYTHONUNBUFFERED=1
 
 RUN  apt-get update \
-  && apt-get install -y python3 python3-dev libgmp-dev python3-pip libzmq5 tzdata \
+  && apt-get install -y python3 python3-dev libgmp-dev python3-pip libzmq5 tzdata libmysqlclient-dev\
   && rm -rf /var/lib/apt/lists/*
 
 ARG TARGET_PATH=/root/.cache/bazel/_bazel_root/17a1cd4fb136f9bc7469e0db6305b35a/execroot/__main__/bazel-out/k8-fastbuild/bin

Makefile

 
 linux_x86_64:
-	#bazel build --config=linux --define cpu=amd64  --define microsoft-apsi=true //:node //:cli
-	bazel build --config=linux --define cpu=amd64  --define microsoft-apsi=true //:node //:cli //:linkcontext //:opt_paillier_c2py
+	bazel build --config=linux_x86_64 //:node //:cli //:linkcontext //:opt_paillier_c2py //:linkcontext
+
+linux_aarch64:
+	bazel build --config=linux_aarch64 //:node //:cli //:linkcontext //:opt_paillier_c2py //:linkcontext 
 
 macos_arm64:
 	bazel build --config=macos --define cpu=arm64 --define microsoft-apsi=true //:node //:cli //:opt_paillier_c2py //:linkcontext

WORKSPACE_CN

@@ -538,7 +538,8 @@ http_archive(
 # APSI
 git_repository(
    name = "mircrosoft_apsi",
-   branch = "bazel_version",
+   #branch = "bazel_version",
+   commit = "44243c1a85435c04ca858279757ca5524dd3c9aa",
    remote = "https://gitee.com/primihub/APSI.git",
 )
 

WORKSPACE_GITHUB

@@ -562,7 +562,8 @@ http_archive(
 # APSI
 git_repository(
     name = "mircrosoft_apsi",
-    branch = "bazel_version",
+    #branch = "bazel_version",
+    commit = "44243c1a85435c04ca858279757ca5524dd3c9aa",
     remote = "https://github.com/primihub/APSI.git",
 )
 

build_local.sh

@@ -9,7 +9,9 @@ fi
 
 bash pre_build.sh
 
-bazel build --config=linux --define cpu=amd64 --define microsoft-apsi=true :node :cli :opt_paillier_c2py :linkcontext
+ARCH=`arch`
+
+bazel build --config=linux_$ARCH :node :cli :opt_paillier_c2py :linkcontext
 
 if [ $? -ne 0 ]; then
     echo "Build failed!!!"

config/node0.yaml

@@ -16,19 +16,24 @@
 version: 1.0
 
 node: "node0"
+#location: "www.primihub.server.com"
+#use_tls: true
 location: "127.0.0.1"
+use_tls: false
+
 grpc_port: 50050
 
+#certificate:
+#  root_ca: "data/cert/ca.crt"
+#  key: "data/cert/node0.key"
+#  cert: "data/cert/node0.crt"
+
 # Use redis by default, set `use_redis: False` to use libp2p
 redis_meta_service:
   redis_addr: "127.0.0.1:6379"
   use_redis: True
   redis_password: "primihub"
 
-# node_keypair:
-#     public_key:
-#     private_key:
-
 # load datasets
 datasets:
   # ABY3 LR test case datasets
@@ -50,10 +55,28 @@ datasets:
     model: "csv"
     source: "data/falcon/dataset/MNIST/input_1"
 
+  # FL homo lr test case datasets
+  - description: "homo_lr_data"
+    model: "csv"
+    source: "data/FL/homo_lr/breast_cancer.csv"
+  - description: "train_homo_lr"
+    model: "csv"
+    source: "data/FL/homo_lr/train_breast_cancer.csv"
+
+  # PSI test case datasets for sqlite database
+  - description: "psi_client_data_db"
+    model: "sqlite"
+    table_name: "psi_client_data"
+    source: "data/client_e.db3"
+
     # Dataset authorization
     # authorization:
     #   - node:
     #     task:
+  # PSI test caset datasets
+  - description: "psi_client_data"
+    model: "csv"
+    source: "data/client_e.csv"
 
 localkv:
   model: "leveldb"

config/node1.yaml

@@ -16,7 +16,12 @@
 version: 1.0
 
 node: "node1"
+#location: "www.primihub.server.com"
+#use_tls: true
+
 location: "127.0.0.1"
+use_tls: false
+
 grpc_port: 50051
 
 # Use redis by default, set `use_redis: False` to use libp2p
@@ -25,6 +30,11 @@ redis_meta_service:
   use_redis: True
   redis_password: "primihub"
 
+#certificate:
+#  root_ca: "data/cert/ca.crt"
+#  key: "data/cert/node0.key"
+#  cert: "data/cert/node0.crt"
+
 # load datasets
 datasets:
   # ABY3 LR test case datasets
@@ -77,6 +87,21 @@ datasets:
     model: "csv"
     source: "data/FL/homo_lr_test.data"
 
+  # FL homo lr test case datasets
+  - description: "train_homo_lr_host"
+    model: "csv"
+    source: "data/FL/homo_lr/train/train_breast_cancer_host.csv"
+  - description: "test_homo_lr"
+    model: "csv"
+    source: "data/FL/homo_lr/test_breast_cancer.csv"
+
+  - description: "train_hetero_xgb_host"
+    model: "csv"
+    source: "data/FL/hetero_xgb/train/train_breast_cancer_host.csv"
+
+  - description: "test_hetero_xgb_host"
+    model: "csv"
+    source: "data/FL/hetero_xgb/test/test_breast_cancer_host.csv"
 
 localkv:
   model: "leveldb"

config/node2.yaml

@@ -16,9 +16,19 @@
 version: 1.0
 
 node: "node2"
+#location: "www.primihub.server.com"
+#use_tls: true
+
 location: "127.0.0.1"
+use_tls: false
+
 grpc_port: 50052
 
+#certificate:
+#  root_ca: "data/cert/ca.crt"
+#  key: "data/cert/node0.key"
+#  cert: "data/cert/node0.crt"
+
 # Use redis by default, set `use_redis: False` to use libp2p
 redis_meta_service:
   redis_addr: "127.0.0.1:6379"
@@ -54,16 +64,30 @@ datasets:
     model: "csv"
     source: "data/FL/wisconsin.data"
 
-  # PSI test caset datasets
-  - description: "psi_client_data"
+  # FL homo lr test case datasets
+  - description: "train_homo_lr_guest"
     model: "csv"
-    source: "data/client_e.csv"
+    source: "data/FL/homo_lr/train/train_breast_cancer_guest.csv"
+
+  ## PSI test caset datasets
+  #- description: "psi_client_data"
+  #  model: "csv"
+  #  source: "data/client_e.csv"
 
   # PSI test case datasets for sqlite database
-  - description: "psi_client_data_db"
-    model: "sqlite"
-    table_name: "psi_client_data"
-    source: "data/client_e.db3"
+  #- description: "psi_client_data_db"
+  #  model: "sqlite"
+  #  table_name: "psi_client_data"
+  #  source: "data/client_e.db3"
+
+  - description: "train_hetero_xgb_guest"
+    model: "csv"
+    source: "data/FL/hetero_xgb/train/train_breast_cancer_guest.csv"
+
+  - description: "test_hetero_xgb_guest"
+    model: "csv"
+    source: "data/FL/hetero_xgb/test/test_breast_cancer_guest.csv"
+
 
 localkv:
   model: "leveldb"

config/primihub_node0.yaml

@@ -2,8 +2,15 @@ version: 1.0
 
 node: "node0"
 location: "172.28.1.10"
+use_tls: false
+
 grpc_port: 50050
 
+#certificate:
+#  root_ca: "data/cert/ca.crt"
+#  key: "data/cert/node0.key"
+#  cert: "data/cert/node0.crt"
+
 # Use redis by default, set `use_redis: False` to use libp2p
 redis_meta_service:
   redis_addr: "redis:6379"
@@ -30,6 +37,14 @@ datasets:
     model: "csv"
     source: "/tmp/falcon/dataset/MNIST/input_1"
 
+  # FL homo lr test case datasets
+  - description: "homo_lr_data"
+    model: "csv"
+    source: "/tmp/FL/homo_lr/breast_cancer.csv"
+  - description: "train_homo_lr"
+    model: "csv"
+    source: "/tmp/FL/homo_lr/train_breast_cancer.csv"
+
 localkv:
   model: "leveldb"
   path: "/data/localdb0"

config/primihub_node1.yaml

@@ -2,8 +2,15 @@ version: 1.0
 
 node: "node1"
 location: "172.28.1.11"
+use_tls: false
+
 grpc_port: 50051
 
+#certificate:
+#  root_ca: "data/cert/ca.crt"
+#  key: "data/cert/node0.key"
+#  cert: "data/cert/node0.crt"
+
 # Use redis by default, set `use_redis: False` to use libp2p
 redis_meta_service:
   redis_addr: "redis:6379"
@@ -64,6 +71,14 @@ datasets:
     model: "csv"
     source: "/tmp/FL/hetero_xgb/test/test_breast_cancer_host.csv"
 
+  # FL homo lr test case datasets
+  - description: "train_homo_lr_host"
+    model: "csv"
+    source: "/tmp/FL/homo_lr/train/train_breast_cancer_host.csv"
+  - description: "test_homo_lr"
+    model: "csv"
+    source: "/tmp/FL/homo_lr/test_breast_cancer.csv"
+
 localkv:
   model: "leveldb"
   path: "/data/localdb1"

config/primihub_node2.yaml

@@ -3,6 +3,12 @@ version: 1.0
 node: "node2"
 location: "172.28.1.12"
 grpc_port: 50052
+use_tls: false
+
+#certificate:
+#  root_ca: "data/cert/ca.crt"
+#  key: "data/cert/node0.key"
+#  cert: "data/cert/node0.crt"
 
 # Use redis by default, set `use_redis: False` to use libp2p
 redis_meta_service:
@@ -35,6 +41,11 @@ datasets:
     model: "csv"
     source: "/tmp/FL/wisconsin.data"
 
+  # FL homo lr test case datasets
+  - description: "train_homo_lr_guest"
+    model: "csv"
+    source: "/tmp/FL/homo_lr/train/train_breast_cancer_guest.csv"
+
   # PSI test caset datasets
   - description: "psi_client_data"
     model: "csv"

data/FL/homo_lr/test/test_breast_cancer_guest.csv

+id,mean radius,mean texture,mean perimeter,mean area,mean smoothness,mean compactness,mean concavity,mean concave points,mean symmetry,mean fractal dimension,radius error,texture error,perimeter error,area error,smoothness error,compactness error,concavity error,concave points error,symmetry error,fractal dimension error,worst radius,worst texture,worst perimeter,worst area,worst smoothness,worst compactness,worst concavity,worst concave points,worst symmetry,worst fractal dimension,y
+301,12.46,19.89,80.43,471.3,0.08451,0.1014,0.0683,0.03099,0.1781,0.06249,0.3642,1.04,2.579,28.32,0.00653,0.03369,0.04712,0.01403,0.0274,0.004651,13.46,23.07,88.13,551.3,0.105,0.2158,0.1904,0.07625,0.2685,0.07764,1
+233,20.51,27.81,134.4,1319.0,0.09159,0.1074,0.1554,0.0834,0.1448,0.05592,0.524,1.189,3.767,70.01,0.00502,0.02062,0.03457,0.01091,0.01298,0.002887,24.47,37.38,162.7,1872.0,0.1223,0.2761,0.4146,0.1563,0.2437,0.08328,0
+506,12.22,20.04,79.47,453.1,0.1096,0.1152,0.08175,0.02166,0.2124,0.06894,0.1811,0.7959,0.9857,12.58,0.006272,0.02198,0.03966,0.009894,0.0132,0.003813,13.16,24.17,85.13,515.3,0.1402,0.2315,0.3535,0.08088,0.2709,0.08839,1
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+512,13.4,20.52,88.64,556.7,0.1106,0.1469,0.1445,0.08172,0.2116,0.07325,0.3906,0.9306,3.093,33.67,0.005414,0.02265,0.03452,0.01334,0.01705,0.004005,16.41,29.66,113.3,844.4,0.1574,0.3856,0.5106,0.2051,0.3585,0.1109,0
+466,13.14,20.74,85.98,536.9,0.08675,0.1089,0.1085,0.0351,0.1562,0.0602,0.3152,0.7884,2.312,27.4,0.007295,0.03179,0.04615,0.01254,0.01561,0.00323,14.8,25.46,100.9,689.1,0.1351,0.3549,0.4504,0.1181,0.2563,0.08174,1
+462,14.4,26.99,92.25,646.1,0.06995,0.05223,0.03476,0.01737,0.1707,0.05433,0.2315,0.9112,1.727,20.52,0.005356,0.01679,0.01971,0.00637,0.01414,0.001892,15.4,31.98,100.4,734.6,0.1017,0.146,0.1472,0.05563,0.2345,0.06464,1
+557,9.423,27.88,59.26,271.3,0.08123,0.04971,0.0,0.0,0.1742,0.06059,0.5375,2.927,3.618,29.11,0.01159,0.01124,0.0,0.0,0.03004,0.003324,10.49,34.24,66.5,330.6,0.1073,0.07158,0.0,0.0,0.2475,0.06969,1
+192,9.72,18.22,60.73,288.1,0.0695,0.02344,0.0,0.0,0.1653,0.06447,0.3539,4.885,2.23,21.69,0.001713,0.006736,0.0,0.0,0.03799,0.001688,9.968,20.83,62.25,303.8,0.07117,0.02729,0.0,0.0,0.1909,0.06559,1
+489,16.69,20.2,107.1,857.6,0.07497,0.07112,0.03649,0.02307,0.1846,0.05325,0.2473,0.5679,1.775,22.95,0.002667,0.01446,0.01423,0.005297,0.01961,0.0017,19.18,26.56,127.3,1084.0,0.1009,0.292,0.2477,0.08737,0.4677,0.07623,0
+555,10.29,27.61,65.67,321.4,0.0903,0.07658,0.05999,0.02738,0.1593,0.06127,0.2199,2.239,1.437,14.46,0.01205,0.02736,0.04804,0.01721,0.01843,0.004938,10.84,34.91,69.57,357.6,0.1384,0.171,0.2,0.09127,0.2226,0.08283,1
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+65,14.78,23.94,97.4,668.3,0.1172,0.1479,0.1267,0.09029,0.1953,0.06654,0.3577,1.281,2.45,35.24,0.006703,0.0231,0.02315,0.01184,0.019,0.003224,17.31,33.39,114.6,925.1,0.1648,0.3416,0.3024,0.1614,0.3321,0.08911,0
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psi_benchmark.sh

 #！/bin/bash
 
 bash pre_build.sh
-bazel build --config=linux :libpsi_test
+bazel build --config=linux_x86_64 :libpsi_test
 
 cd ./bazel-bin
 

python/primihub/FL/model/logistic_regression/hetero_lr_base.py

+import numpy as np
+from sklearn import metrics
+from collections import Iterable
+
+
+def dloss(p, y):
+    z = p * y
+    if z > 18.0:
+        return np.exp(-z) * -y
+    if z < -18.0:
+        return -y
+    return -y / (np.exp(z) + 1.0)
+
+
+def batch_yield(x, y, batch_size):
+    for i in range(0, x.shape[0], batch_size):
+        yield (x[i:i + batch_size], y[i:i + batch_size])
+
+
+def trucate_geometric_thres(x, clip_thres, variation, times=2):
+    if isinstance(x, Iterable):
+        norm_x = np.sqrt(sum(x * x))
+        n = len(x)
+    else:
+        norm_x = abs(x)
+        n = 1
+
+    clip_thres = np.max([1, norm_x / clip_thres])
+    clip_x = x / clip_thres
+
+    dp_noise = None
+
+    for _ in range(2 * times):
+        cur_noise = np.random.normal(0, clip_thres * variation, n)
+
+        if dp_noise is None:
+            dp_noise = cur_noise
+        else:
+            dp_noise += cur_noise
+
+    dp_noise /= np.sqrt(2 * times)
+
+    dp_x = clip_x + dp_noise
+
+    return dp_x
+
+
+class HeteroLrBase:
+
+    def __init__(self,
+                 learning_rate=0.01,
+                 alpha=0.0001,
+                 epochs=10,
+                 penalty="l2",
+                 batch_size=64,
+                 optimal_method=None,
+                 update_type=None,
+                 loss_type='log',
+                 random_state=2023,
+                 clip_thres=1.0,
+                 noise_variation=1.0):
+        self.learning_rate = learning_rate
+        self.alpha = alpha
+        self.epochs = epochs
+        self.batch_size = batch_size
+        self.penalty = penalty
+        self.optimal_method = optimal_method
+        self.random_state = random_state
+        self.update_type = update_type
+        self.loss_type = loss_type
+        self.clip_thres = clip_thres
+        self.noise_variation = noise_variation
+        self.theta = 0
+
+    def fit(self):
+        pass
+
+    def predict(self):
+        pass
+
+    def loss(self, y_hat, y_true):
+        if self.loss_type == 'log':
+            y_prob = self.sigmoid(y_hat)
+
+            return metrics.log_loss(y_true, y_prob)
+
+        elif self.loss == "squarederror":
+            return metrics.mean_squared_error(
+                y_true, y_hat)  # mse don't activate inputs
+        else:
+            raise KeyError('The type is not implemented!')
+
+
+class PlainLR:
+
+    def __init__(self,
+                 learning_rate=0.01,
+                 alpha=0.0001,
+                 epochs=10,
+                 penalty="l2",
+                 batch_size=64,
+                 optimal_method=None,
+                 update_type=None,
+                 loss_type='log',
+                 random_state=2023):
+        self.learning_rate = learning_rate
+        self.alpha = alpha
+        self.epochs = epochs
+        self.batch_size = batch_size
+        self.penalty = penalty
+        self.optimal_method = optimal_method
+        self.random_state = random_state
+        self.update_type = update_type
+        self.loss_type = loss_type
+        self.theta = 0
+
+    def add_intercept(self, x):
+        intercept = np.ones((x.shape[0], 1))
+        return np.concatenate((intercept, x), axis=1)
+
+    def sigmoid(self, x):
+        return 1.0 / (1 + np.exp(-x))
+
+    def predict_prob(self, x):
+        return self.sigmoid(np.dot(x, self.theta))
+
+    def predict(self, x):
+        preds = self.predict_prob(x)
+        preds[preds <= 0.5] = 0
+        preds[preds > 0.5] = 1
+
+        return preds
+
+    def gradient(self, x, y):
+        h = self.predict_prob(x)
+        if self.penalty == "l2":
+            grad = (np.dot(x.T, (h - y)) / x.shape[0] + self.alpha * self.theta
+                   )  #/ x.shape[0]
+        elif self.penalty == "l1":
+            raise ValueError("It's not implemented now!")
+
+        else:
+            grad = np.dot(x.T, (h - y)) / x.shape[0]  #/ x.shape[0]
+
+        return grad
+
+    def update_lr(self, current_epoch, type="sqrt"):
+        if type == "sqrt":
+            self.learning_rate /= np.sqrt(current_epoch + 1)
+        else:
+            typw = np.sqrt(1.0 / np.sqrt(self.alpha))
+            initial_eta0 = typw / max(1.0, dloss(-typw, 1.0))
+            optimal_init = 1.0 / (initial_eta0 * self.alpha)
+
+            self.learning_rate = 1.0 / (self.alpha *
+                                        (optimal_init + current_epoch + 1))
+
+    def simple_gd(self, x, y):
+        grad = self.gradient(x, y)
+        self.theta -= self.learning_rate * grad
+        # print("======", self.theta, grad, self.learning_rate)
+
+    def batch_gd(self, x, y):
+        for batch_x, bathc_y in batch_yield(x, y, self.batch_size):
+            grad = self.gradient(batch_x, bathc_y)
+
+            self.theta -= self.learning_rate * grad
+
+    def fit(self, x, y):
+        x = self.add_intercept(x)
+        if self.batch_size < 0:
+            self.batch_size = x.shape[0]
+
+        np.random.seed(self.random_state)
+        self.theta = np.random.rand(x.shape[1])
+        total_loss = []
+
+        for i in range(self.epochs):
+            self.update_lr(i, type=self.update_type)
+
+            if self.optimal_method == "simple":
+                self.simple_gd(x, y)
+
+            else:
+                self.batch_gd(x, y)
+
+            print("current iteration and theta", i, self.theta)
+
+            y_hat = np.dot(x, self.theta)
+            current_loss = self.loss(y_hat, y)
+            total_loss.append(current_loss)
+            # print("current iteration and loss", i, current_loss)
+        print("loss", total_loss)
+
+    def loss(self, y_hat, y_true):
+        if self.loss_type == 'log':
+            y_prob = self.sigmoid(y_hat)
+
+            return metrics.log_loss(y_true, y_prob)
+
+        elif self.loss == "squarederror":
+            return metrics.mean_squared_error(
+                y_true, y_hat)  # mse don't activate inputs
+        else:
+            raise KeyError('The type is not implemented!')

python/primihub/FL/model/logistic_regression/hetero_lr_guest.py

+import numpy as np
+from primihub.FL.model.logistic_regression.hetero_lr_base import HeteroLrBase, batch_yield, trucate_geometric_thres
+
+
+class HeterLrGuest(HeteroLrBase):
+
+    def __init__(self,
+                 learning_rate=0.01,
+                 alpha=0.0001,
+                 epochs=10,
+                 penalty="l2",
+                 batch_size=64,
+                 optimal_method=None,
+                 update_type=None,
+                 loss_type='log',
+                 random_state=2023,
+                 guest_channel=None,
+                 add_noise=True):
+        super().__init__(learning_rate, alpha, epochs, penalty, batch_size,
+                         optimal_method, update_type, loss_type, random_state)
+        self.channel = guest_channel
+        self.add_noise = add_noise
+
+    def predict(self, x):
+        guest_part = np.dot(x, self.theta)
+        # if self.add_noise:
+        #     guest_part = trucate_geometric_thres(guest_part, self.clip_thres,
+        #                                          self.noise_variation)
+        self.channel.sender("guest_part", guest_part)
+
+    def gradient(self, x):
+        error = self.channel.recv('error')
+        if self.penalty == "l2":
+            grad = (np.dot(x.T, error) / x.shape[0] + self.alpha * self.theta
+                   )  #/ x.shape[0]
+        elif self.penalty == "l1":
+            raise ValueError("It's not implemented now!")
+
+        else:
+            grad = np.dot(x.T, error) / x.shape[0]  #/ x.shape[0]
+
+        return grad
+
+    def batch_gd(self, x):
+        for batch_x, _ in batch_yield(x, x, self.batch_size):
+            self.predict(batch_x)
+            grad = self.gradient(batch_x)
+            self.theta -= self.learning_rate * grad
+
+    def simple_gd(self, x):
+        self.predict(x)
+        grad = self.gradient(x)
+        self.theta -= self.learning_rate * grad
+
+    def fit(self, x):
+        if self.batch_size < 0:
+            self.batch_size = x.shape[0]
+
+        self.theta = np.zeros(x.shape[1])
+        for i in range(self.epochs):
+            self.learning_rate = self.channel.recv("learning_rate")
+
+            if self.optimal_method == "simple":
+                self.simple_gd(x)
+
+            else:
+                self.batch_gd(x)
+
+            self.predict(x)
+
+            is_converged = self.channel.recv('is_converged')
+
+            if is_converged:
+                break

python/primihub/FL/model/logistic_regression/hetero_lr_host.py

+import numpy as np
+from primihub.FL.model.logistic_regression.hetero_lr_base import HeteroLrBase, batch_yield, dloss, trucate_geometric_thres
+
+
+class HeterLrHost(HeteroLrBase):
+
+    def __init__(self,
+                 learning_rate=0.01,
+                 alpha=0.0001,
+                 epochs=10,
+                 penalty="l2",
+                 batch_size=64,
+                 optimal_method=None,
+                 update_type=None,
+                 loss_type='log',
+                 random_state=2023,
+                 host_channel=None,
+                 add_noise=True,
+                 tol=0.001):
+        super().__init__(learning_rate, alpha, epochs, penalty, batch_size,
+                         optimal_method, update_type, loss_type, random_state)
+        self.channel = host_channel
+        self.add_noise = add_noise
+        self.tol = tol
+
+    def add_intercept(self, x):
+        intercept = np.ones((x.shape[0], 1))
+        return np.concatenate((intercept, x), axis=1)
+
+    def sigmoid(self, x):
+        return 1.0 / (1 + np.exp(-x))
+
+    def predict_raw(self, x):
+        host_part = np.dot(x, self.theta)
+        guest_part = self.channel.recv("guest_part")
+        h = host_part + guest_part
+
+        return h
+
+    def predict(self, x):
+        preds = self.sigmoid(self.predict_raw(x))
+        preds[preds <= 0.5] = 0
+        preds[preds > 0.5] = 1
+
+        return preds
+
+    def update_lr(self, current_epoch, type="sqrt"):
+        if type == "sqrt":
+            self.learning_rate /= np.sqrt(current_epoch + 1)
+        else:
+            typw = np.sqrt(1.0 / np.sqrt(self.alpha))
+            initial_eta0 = typw / max(1.0, dloss(-typw, 1.0))
+            optimal_init = 1.0 / (initial_eta0 * self.alpha)
+
+            self.learning_rate = 1.0 / (self.alpha *
+                                        (optimal_init + current_epoch + 1))
+
+    def gradient(self, x, y):
+        h = self.sigmoid(self.predict_raw(x))
+        error = h - y
+        # self.channel.sender('error', error)
+        if self.add_noise:
+            # nois_error = trucate_geometric_thres(error,
+            #                                      clip_thres=self.clip_thres,
+            #                                      variation=self.noise_variation)
+
+            # add adaptive-noise for error
+            error_std = np.std(error)
+            noise = np.random.normal(0, error_std, error.shape)
+            nois_error = error + noise
+        else:
+            nois_error = error
+        self.channel.sender('error', nois_error)
+
+        if self.penalty == "l2":
+            grad = (np.dot(x.T, error) / x.shape[0] + self.alpha * self.theta
+                   )  #/ x.shape[0]
+        elif self.penalty == "l1":
+            raise ValueError("It's not implemented now!")
+
+        else:
+            grad = np.dot(x.T, error) / x.shape[0]  #/ x.shape[0]
+
+        return grad
+
+    def batch_gd(self, x, y):
+        for batch_x, bathc_y in batch_yield(x, y, self.batch_size):
+            grad = self.gradient(batch_x, bathc_y)
+            self.theta -= self.learning_rate * grad
+
+    def simple_gd(self, x, y):
+        grad = self.gradient(x, y)
+        self.theta -= self.learning_rate * grad
+
+    def fit(self, x, y):
+        x = self.add_intercept(x)
+        if self.batch_size < 0:
+            self.batch_size = x.shape[0]
+
+        self.theta = np.zeros(x.shape[1])
+        pre_loss = None
+        is_converged = False
+        for i in range(self.epochs):
+            self.update_lr(i, type=self.update_type)
+            self.channel.sender("learning_rate", self.learning_rate)
+
+            if self.optimal_method == "simple":
+                self.simple_gd(x, y)
+
+            else:
+                self.batch_gd(x, y)
+
+            y_hat = self.predict_raw(x)
+            cur_loss = self.loss(y_hat, y)
+
+            if pre_loss is None:
+                pre_loss = cur_loss
+
+            else:
+                loss_diff = abs(pre_loss - cur_loss)
+                pre_loss = cur_loss
+
+                if loss_diff < self.tol:
+                    is_converged = True
+
+            self.channel.sender('is_converged', is_converged)
+
+            if is_converged:
+                break
+
+        pred_prob = self.sigmoid(y_hat)
+        preds = (pred_prob > 0.5).astype('int')
+        acc = sum((preds == y).astype('int')) / len(y)
+        print("acc: ", acc)

python/primihub/FL/model/logistic_regression/hetero_lr_run.py

+import primihub as ph
+import pandas as pd
+from primihub import dataset, context
+from primihub.utils.net_worker import GrpcServer
+from primihub.FL.model.logistic_regression.hetero_lr_host import HeterLrHost
+from primihub.FL.model.logistic_regression.hetero_lr_guest import HeterLrGuest
+from sklearn.preprocessing import StandardScaler, MinMaxScaler
+
+config = {
+    "learning_rate": 0.01,
+    'alpha': 0.0001,
+    "epochs": 50,
+    "penalty": "l2",
+    "optimal_method": "Complex",
+    "random_state": 2023,
+    "host_columns": None,
+    "guest_columns": None,
+    "scale_type": 'z-score',
+    "batch_size": 512
+}
+
+
+@ph.context.function(
+    role='host',
+    protocol='hetero_lr',
+    datasets=['train_hetero_xgb_host'
+             ],  # ['train_hetero_xgb_host'],  #, 'test_hetero_xgb_host'],
+    port='8000',
+    task_type="classification")
+def lr_host_logic():
+    role_node_map = ph.context.Context.get_role_node_map()
+    node_addr_map = ph.context.Context.get_node_addr_map()
+    dataset_map = ph.context.Context.dataset_map
+    taskId = ph.context.Context.params_map['taskid']
+    jobId = ph.context.Context.params_map['jobid']
+
+    host_nodes = role_node_map["host"]
+    host_port = node_addr_map[host_nodes[0]].split(":")[1]
+    host_ip = node_addr_map[host_nodes[0]].split(":")[0]
+
+    guest_nodes = role_node_map["guest"]
+    guest_ip, guest_port = node_addr_map[guest_nodes[0]].split(":")
+
+    data_key = list(dataset_map.keys())[0]
+    data = ph.dataset.read(dataset_key=data_key).df_data
+    print("ports: ", guest_port, host_port)
+    #data = pd.read_csv("/home/xusong/data/epsilon_normalized.host", header=0)
+
+    host_cols = config['host_columns']
+
+    if host_cols is not None:
+        data = data[host_cols]
+
+    if 'id' in data.columns:
+        data.pop('id')
+    Y = data.pop('y').values
+    X_host = data.copy()
+
+    # grpc server initialization
+    host_channel = GrpcServer(remote_ip=guest_ip,
+                              local_ip=host_ip,
+                              remote_port=guest_port,
+                              local_port=host_port,
+                              context=ph.context.Context)
+
+    lr_host = HeterLrHost(learning_rate=config['learning_rate'],
+                          alpha=config['alpha'],
+                          epochs=config['epochs'],
+                          optimal_method=config['optimal_method'],
+                          random_state=config['random_state'],
+                          host_channel=host_channel,
+                          add_noise=False,
+                          batch_size=config['batch_size'])
+    scale_type = config['scale_type']
+
+    scale_type = config['scale_type']
+
+    if scale_type is not None:
+        if scale_type == "z-score":
+            std = StandardScaler()
+        else:
+            std = MinMaxScaler()
+        scale_x = std.fit_transform(X_host)
+
+    else:
+        scale_x = X_host.copy()
+
+    lr_host.fit(scale_x, Y)
+
+
+@ph.context.function(
+    role='guest',
+    protocol='heter_lr',
+    datasets=[
+        'train_hetero_xgb_guest'  #'five_thous_guest'
+    ],  #['train_hetero_xgb_guest'],  #, 'test_hetero_xgb_guest'],
+    port='9000',
+    task_type="classification")
+def lr_guest_logic(cry_pri="paillier"):
+    role_node_map = ph.context.Context.get_role_node_map()
+    node_addr_map = ph.context.Context.get_node_addr_map()
+    dataset_map = ph.context.Context.dataset_map
+    taskId = ph.context.Context.params_map['taskid']
+    jobId = ph.context.Context.params_map['jobid']
+
+    guest_nodes = role_node_map["guest"]
+    guest_port = node_addr_map[guest_nodes[0]].split(":")[1]
+    guest_ip = node_addr_map[guest_nodes[0]].split(":")[0]
+
+    host_nodes = role_node_map["host"]
+    host_ip, host_port = node_addr_map[host_nodes[0]].split(":")
+
+    data_key = list(dataset_map.keys())[0]
+    data = ph.dataset.read(dataset_key=data_key).df_data
+    print("ports: ", host_port, guest_port)
+    # data = pd.read_csv("/home/xusong/data/epsilon_normalized.guest", header=0)
+
+    guest_cols = config['guest_columns']
+    if guest_cols is not None:
+        data = data[guest_cols]
+
+    if 'id' in data.columns:
+        data.pop('id')
+
+    X_guest = data
+    guest_channel = GrpcServer(remote_ip=host_ip,
+                               remote_port=host_port,
+                               local_ip=guest_ip,
+                               local_port=guest_port,
+                               context=ph.context.Context)
+
+    lr_guest = HeterLrGuest(learning_rate=config['learning_rate'],
+                            alpha=config['alpha'],
+                            epochs=config['epochs'],
+                            optimal_method=config['optimal_method'],
+                            random_state=config['random_state'],
+                            guest_channel=guest_channel,
+                            batch_size=config['batch_size'])
+
+    scale_type = config['scale_type']
+
+    if scale_type is not None:
+        if scale_type == "z-score":
+            std = StandardScaler()
+        else:
+            std = MinMaxScaler()
+
+        scale_x = std.fit_transform(X_guest)
+
+    else:
+        scale_x = X_guest.copy()
+
+    lr_guest.fit(scale_x)

python/primihub/FL/model/logistic_regression/homo_lr_base.py

 # -*- coding:utf-8
 import numpy as np
-from primihub.FL.feature_engineer.onehot_encode import HorOneHotEncoder
-from sklearn.preprocessing import MinMaxScaler
-
 
 class LRModel:
-    def __init__(self, X, y, category, w=None):
-        self.w_size = X.shape[1] + 1
-        self.coef = None
-        self.intercept = None
-        self.theta = None
-        self.one_vs_rest_theta = np.random.uniform(-0.5, 0.5, (category, self.w_size))
-        if w is not None:
-            self.theta = w
+    # l2 regularization by default, alpha is the penalty parameter
+    def __init__(self, X, y, category, learning_rate=0.2, alpha=0.0001):
+        self.learning_rate = learning_rate
+        self.alpha = alpha # regularization parameter
+        self.t = 0 # iteration number, used for learning rate decay
+
+        if category == 2:
+            self.theta = np.random.uniform(-0.5, 0.5, (X.shape[1] + 1,))
+            self.multi_class = False
         else:
-            # init model parameters
-            self.theta = np.random.uniform(-0.5, 0.5, (self.w_size,))
+            self.one_vs_rest_theta = np.random.uniform(-0.5, 0.5, (category, X.shape[1] + 1))
+            self.multi_class = True
+        
+        # 'optimal' learning rate refer to sklearn SGDClassifier
+        def dloss(p, y):
+            z = p * y
+            if z > 18.0:
+                return np.exp(-z) * -y
+            if z < -18.0:
+                return -y
+            return -y / (np.exp(z) + 1.0)
+
+        typw = np.sqrt(1.0 / np.sqrt(alpha))
+        # computing eta0, the initial learning rate
+        initial_eta0 = typw / max(1.0, dloss(-typw, 1.0))
+        # initialize t such that eta at first sample equals eta0
+        self.optimal_init = 1.0 / (initial_eta0 * alpha)
 
         # if encrypted == True:
         #     self.theta = self.utils.encrypt_vector(public_key, self.theta)
 
-    @staticmethod
-    def normalization(x):
-        """
-        data normalization
-        """
-        scaler = MinMaxScaler()
-        scaler = scaler.fit(x)
-        x = scaler.transform(x)
-        return x
-
     def sigmoid(self, x):
-        x = np.array(x, dtype=np.float64)
         y = 1.0 / (1.0 + np.exp(-x))
         return y
 
-    def loss_func(self, theta, x_b, y):
-        """
-        loss function
-        :param theta: intercept and coef
-        :param x_b: training data
-        :param y: label
-        :return:
-        """
-        p_predict = self.sigmoid(x_b.dot(theta))
+    def get_theta(self):
+        return self.theta
+
+    def set_theta(self, theta):
+        if not isinstance(theta, np.ndarray):
+            theta = np.array(theta)
+        self.theta = theta
+
+    def loss(self, x, y):
+        temp = x.dot(self.theta[1:]) + self.theta[0]
         try:
-            return -np.sum(y * np.log(p_predict) + (1 - y) * np.log(1 - p_predict))
+            return (np.maximum(temp, 0.).sum() - y.dot(temp) +
+                    np.log(1 + np.exp(-np.abs(temp))).sum() +
+                    0.5 * self.alpha * self.theta.dot(self.theta)) / x.shape[0]
         except:
             return float('inf')
 
-    def d_loss_func(self, theta, x_b, y):
-        out = self.sigmoid(x_b.dot(theta))
-        return x_b.T.dot(out - y) / len(x_b)
+    def compute_grad(self, x, y):
+        temp = self.predict_prob(x) - y
+        return (np.concatenate((temp.sum(keepdims=True), x.T.dot(temp)))
+                + self.alpha * self.theta) / x.shape[0]
 
-    def gradient_descent(self, x_b, y, theta, eta):
+    def gradient_descent(self, x, y):
+        grad = self.compute_grad(x, y)
+        self.theta -= self.learning_rate * grad
+        
+    def gradient_descent_olr(self, x, y):
         """
-        :param x_b: training data
-        :param y: label
-        :param theta: model parameters
-        :param eta: learning rate
-        :return:
+        optimal learning rate
         """
-        gradient = self.d_loss_func(theta, x_b, y)
-        theta = theta - eta * gradient
-        return theta
-
-    def fit(self, train_data, train_label, theta, eta=0.01,):
-        assert train_data.shape[0] == train_label.shape[0], "The length of the training data set shall " \
-                                                            "be consistent with the length of the label"
-        x_b = np.hstack([np.ones((train_data.shape[0], 1)), train_data])
-
-        self.theta = self.gradient_descent(x_b, train_label, theta, eta)
-        self.intercept = self.theta[0]
-        self.coef = self.theta[1:]
-        return self.theta
+        grad = self.compute_grad(x, y)
+        learning_rate = 1.0 / (self.alpha * (self.optimal_init + self.t))
+        self.t += 1
+        self.theta -= learning_rate * grad
     
-    def predict_prob(self, x_predict):
-        x_b = np.hstack([np.ones((len(x_predict), 1)), x_predict])
-        return self.sigmoid(x_b.dot(self.theta))
+    def fit(self, x, y):
+        self.gradient_descent_olr(x, y)
 
-    def predict(self, x_predict):
-        """
-        classification
-        """
-        prob = self.predict_prob(x_predict)
+    def predict_prob(self, x):
+        return self.sigmoid(x.dot(self.theta[1:]) + self.theta[0])
+
+    def predict(self, x):
+        prob = self.predict_prob(x)
         return np.array(prob > 0.5, dtype='int')
 
     def one_vs_rest(self, X, y, k):
@@ -111,3 +107,4 @@ class LRModel:
 # def load_dummies(self, union_cats_len, union_cats_idxs):
 #     self.onehot_encoder.cats_len = union_cats_len
 #     self.onehot_encoder.cats_idxs = union_cats_idxs
+

python/primihub/FL/model/logistic_regression/homo_lr_dpsgd.py

+import primihub as ph
+from primihub.FL.model.logistic_regression.homo_lr_dev import run_party
+
+
+config = {
+    'mode': 'DPSGD',
+    'delta': 1e-3,
+    'noise_multiplier': 2.0,
+    'l2_norm_clip': 1.0,
+    'secure_mode': True,
+    'learning_rate': 'optimal',
+    'alpha': 0.0001,
+    'batch_size': 50,
+    'max_iter': 100,
+    'category': 2,
+    'feature_names': None,
+}
+
+
+@ph.context.function(role='arbiter',
+                     protocol='lr',
+                     datasets=['train_homo_lr'],
+                     port='9010',
+                     task_type="lr-train")
+def run_arbiter_party():
+    run_party('arbiter', config)
+
+
+@ph.context.function(role='host',
+                     protocol='lr',
+                     datasets=['train_homo_lr_host'],
+                     port='9020',
+                     task_type="lr-train")
+def run_host_party():
+    run_party('host', config)
+
+
+@ph.context.function(role='guest',
+                     protocol='lr',
+                     datasets=['train_homo_lr_guest'],
+                     port='9030',
+                     task_type="lr-train")
+def run_guest_party():
+    run_party('guest', config)

python/primihub/FL/model/logistic_regression/homo_lr_paillier.py

+import primihub as ph
+from primihub.FL.model.logistic_regression.homo_lr_dev import run_party
+
+
+config = {
+    'mode': 'Paillier',
+    'n_length': 1024,
+    'learning_rate': 'optimal',
+    'alpha': 0.01,
+    'batch_size': 100,
+    'max_iter': 50,
+    'n_iter_no_change': 5,
+    'compare_threshold': 1e-6,
+    'category': 2,
+    'feature_names': None,
+}
+
+
+@ph.context.function(role='arbiter',
+                     protocol='lr',
+                     datasets=['train_homo_lr'],
+                     port='9010',
+                     task_type="lr-train")
+def run_arbiter_party():
+    run_party('arbiter', config)
+
+
+@ph.context.function(role='host',
+                     protocol='lr',
+                     datasets=['train_homo_lr_host'],
+                     port='9020',
+                     task_type="lr-train")
+def run_host_party():
+    run_party('host', config)
+
+
+@ph.context.function(role='guest',
+                     protocol='lr',
+                     datasets=['train_homo_lr_guest'],
+                     port='9030',
+                     task_type="lr-train")
+def run_guest_party():
+    run_party('guest', config)

python/requirements.txt

@@ -25,3 +25,5 @@ transformers
 requests
 line_profiler
 matplotlib
+
+dp-accounting==0.3.0

src/primihub/algorithm/base.h

@@ -37,6 +37,9 @@ class AlgorithmBase {
     virtual int execute() = 0;
     virtual int finishPartyComm() = 0;
     virtual int saveModel() = 0;
+    std::shared_ptr<DatasetService>& datasetService() {
+      return dataset_service_;
+    }
 
   protected:
     std::shared_ptr<DatasetService> dataset_service_;

src/primihub/task/semantic/keyword_pir_client_task.h

@@ -78,6 +78,7 @@ class KeywordPIRClientTask : public TaskBase {
 
  private:
     std::string dataset_path_;
+    std::string dataset_id_;
     std::string result_file_path_;
     std::string server_address_;
     bool recv_query_data_direct{false};