.github/workflows/backup.yaml

@@ -9,6 +9,8 @@ jobs:
   sync:
     runs-on: ubuntu-latest
 
+    if: github.event.repository.fork == 'false'
+
     steps:
 
     - name: Sync to Gitee

Dockerfile

@@ -38,7 +38,15 @@ RUN bash pre_build.sh \
     //:cli \
     //src/primihub/pybind_warpper:opt_paillier_c2py \
     //src/primihub/pybind_warpper::linkcontext \
-  && tar zcf /opt/bazel-bin.tar.gz --exclude=*_objs ./bazel-bin/*
+  && tar zcf bazel-bin.tar.gz bazel-bin/cli \
+             bazel-bin/node \
+             bazel-bin/py_main \
+             bazel-bin/src/primihub/pybind_warpper/opt_paillier_c2py.so \
+             bazel-bin/src/primihub/pybind_warpper/linkcontext.so \
+             python \
+             config \
+             example \
+             data
 
 FROM ubuntu:20.04 as runner
 
@@ -50,23 +58,15 @@ RUN apt-get update \
   && rm -rf /var/lib/apt/lists/*
 
 COPY --from=builder /opt/bazel-bin.tar.gz /opt/bazel-bin.tar.gz
-# Copy test data files to /tmp/
-COPY --from=builder /src/data /app/data
-# Copy all test config files to /app/config
-COPY --from=builder /src/config /app/config
-# Copy primihub python sources to /app and setup to system python3
-COPY --from=builder /src/python /app/python
 COPY --from=builder /src/src/primihub/protos/ /app/src/primihub/protos/
 
 WORKDIR /app
 
-RUN tar zxf /opt/bazel-bin.tar.gz \
-  && mkdir log
-
 # Copy opt_paillier_c2py.so linkcontext.so to /app/python, this enable setup.py find it.
-RUN mv $TARGET_PATH/src/primihub/pybind_warpper/opt_paillier_c2py.so /app/python/ \
-  && mv $TARGET_PATH/src/primihub/pybind_warpper/linkcontext.so /app/python/ \
-  && ln -s bazel-bin/node primihub-node && ln -s bazel-bin/cli primihub-cli
+RUN tar zxf /opt/bazel-bin.tar.gz \
+  && mkdir log \
+  && ln -s bazel-bin/node primihub-node \
+  && ln -s bazel-bin/cli primihub-cli
 
 WORKDIR /app/python
 

Dockerfile.local

@@ -10,27 +10,16 @@ RUN  apt-get update \
   && ln -fs /usr/share/zoneinfo/Asia/Shanghai /etc/localtime \
   && rm -rf /var/lib/apt/lists/*
 
-ARG TARGET_PATH=/root/.cache/bazel/_bazel_root/17a1cd4fb136f9bc7469e0db6305b35a/execroot/__main__/bazel-out/k8-fastbuild/bin
-
-WORKDIR $TARGET_PATH
-# Copy binaries to TARGET_PATH
-COPY . ./
-
 # Change WorkDir to /app
 WORKDIR /app
 
+ADD bazel-bin.tar.gz ./
+COPY src/primihub/protos/ src/primihub/protos/
 # Make symlink to primihub-node & primihub-cli
 RUN mkdir log \
-  && ln -s $TARGET_PATH/node /app/primihub-node && ln -s $TARGET_PATH/cli /app/primihub-cli
-COPY ./example ./example
-# Copy all test config files to /app
-COPY ./data ./data
-COPY ./config ./config
-# Copy primihub python sources to /app and setup to system python3
-COPY ./python ./python
-COPY ./src/primihub/protos/ ./src/primihub/protos/
-RUN cp $TARGET_PATH/src/primihub/pybind_warpper/opt_paillier_c2py.so /app/python/
-RUN cp $TARGET_PATH/src/primihub/pybind_warpper/linkcontext.so /app/python/
+  && ln -s bazel-bin/node primihub-node \
+  && ln -s bazel-bin/cli primihub-cli
+
 WORKDIR /app/python
 RUN python3 -m pip install --upgrade pip \
   && python3 -m pip install -r requirements.txt -i https://pypi.douban.com/simple/ \

Dockerfile.release

@@ -11,19 +11,13 @@ RUN apt-get update \
 WORKDIR /app
 
 COPY primihub-linux-amd64.tar.gz primihub-linux-arm64.tar.gz /opt/
-
-COPY example ./example
-COPY data/ ./data
-COPY config/ ./config
-COPY python/ ./python
 COPY src/primihub/protos/ src/primihub/protos/
 
 RUN ARCH=`arch | sed s/aarch64/arm64/ | sed s/x86_64/amd64/` \
   && tar zxf /opt/primihub-linux-${ARCH}.tar.gz \
   && mkdir log \
-  && mv bazel-bin/src/primihub/pybind_warpper/opt_paillier_c2py.so python/ \
-  && mv bazel-bin/src/primihub/pybind_warpper/linkcontext.so python/ \
-  && ln -s bazel-bin/node primihub-node && ln -s bazel-bin/cli primihub-cli
+  && ln -s bazel-bin/node primihub-node \
+  && ln -s bazel-bin/cli primihub-cli
 
 WORKDIR /app/python
 

WORKSPACE_GITHUB

@@ -423,11 +423,14 @@ new_git_repository(
 )
 
 http_archive(
-    name = "com_github_gmp",
-    build_file = "//bazel:gmp.BUILD",
-    #sha256 = "87b565e89a9a684fe4ebeeddb8399dce2599f9c9049854ca8c0dfbdea0e21912",
-    strip_prefix = "gmp-6.2.1",
-    urls = ["https://gmplib.org/download/gmp/gmp-6.2.1.tar.xz"],
+  name = "com_github_gmp",
+  build_file = "//bazel:gmp.BUILD",
+  #sha256 = "87b565e89a9a684fe4ebeeddb8399dce2599f9c9049854ca8c0dfbdea0e21912",
+  strip_prefix = "gmp-6.2.1",
+  urls = [
+    "https://primihub.oss-cn-beijing.aliyuncs.com/tools/gmp-6.2.1.tar.xz",
+    "https://gmplib.org/download/gmp/gmp-6.2.1.tar.xz"
+  ],
 )
 
 # Google dense_hash_set

bazel/BUILD.eigen

@@ -8,13 +8,16 @@ filegroup(
 )
 
 cmake(
-    name = "eigen",
-    cache_entries = {
-        "CMAKE_C_FLAGS": "-fPIC",
-    },
-    lib_source = ":src",
-    out_headers_only = True,
-    includes = ["eigen3/",],
-    install = True,
-    visibility = ["//visibility:public"],
-)
+  name = "eigen",
+  cache_entries = {
+    "CMAKE_C_FLAGS": "-fPIC",
+  },
+  build_args = [
+    "-j4",
+  ],
+  lib_source = ":src",
+  out_headers_only = True,
+  includes = ["eigen3/",],
\ No newline at end of file
+  install = True,
+  visibility = ["//visibility:public"],
+)

bazel/BUILD.kuku

@@ -9,13 +9,16 @@ filegroup(
 )
 
 cmake(
-   name = "kuku",
-   cache_entries = {
-      "CMAKE_INSTALL_LIBDIR": "lib",
-   },
-   lib_source = ":src",
-   out_include_dir = "include/Kuku-2.1",
-   out_static_libs = ["libkuku-2.1.a"],
-   visibility = ["//visibility:public"],
-   tags = ["requires-network"],
+  name = "kuku",
+  cache_entries = {
+    "CMAKE_INSTALL_LIBDIR": "lib",
+  },
+  build_args = [
+    "-j4",
+  ],
+  lib_source = ":src",
+  out_include_dir = "include/Kuku-2.1",
+  out_static_libs = ["libkuku-2.1.a"],
+  visibility = ["//visibility:public"],
+  tags = ["requires-network"],
 )

bazel/BUILD.leveldb

@@ -7,21 +7,24 @@ filegroup(
 )
 
 cmake(
-    name = "leveldb",
-    visibility = ["//visibility:public"],
-    cache_entries = {
-        "CMAKE_BUILD_TYPE": "Release",
-        "BUILD_SHARED_LIBS": "OFF",
-        # Turning off building tests and benchmarks as those would
-        # requires first pulling down those git submodules (which
-        # would also require using 'git_repository' instead of
-        # 'http_archive'.
-        "LEVELDB_BUILD_TESTS": "OFF",
-        "LEVELDB_BUILD_BENCHMARKS": "OFF",
-        "CMAKE_INSTALL_LIBDIR": "lib",
-    },
-    lib_source = ":all",
-    out_static_libs = ["libleveldb.a"],
+  name = "leveldb",
+  visibility = ["//visibility:public"],
+  cache_entries = {
+    "CMAKE_BUILD_TYPE": "Release",
+    "BUILD_SHARED_LIBS": "OFF",
+    # Turning off building tests and benchmarks as those would
+    # requires first pulling down those git submodules (which
+    # would also require using 'git_repository' instead of
+    # 'http_archive'.
+    "LEVELDB_BUILD_TESTS": "OFF",
+    "LEVELDB_BUILD_BENCHMARKS": "OFF",
+    "CMAKE_INSTALL_LIBDIR": "lib",
+  },
+  build_args = [
+    "-j4",
+  ],
+  lib_source = ":all",
+  out_static_libs = ["libleveldb.a"],
 )
 
 #cc_library(

bazel/BUILD.relic

@@ -11,6 +11,9 @@ cmake(
   cache_entries = {
     "CMAKE_INSTALL_LIBDIR": "lib",
   },
+  build_args = [
+    "-j4",
+  ],
   linkopts = [
     "-lpthread",
   ],

build_local.sh

@@ -17,7 +17,6 @@ bash pre_build.sh
 
 ARCH=`arch`
 
-
 bazel build --config=linux_$ARCH --define enable_mysql_driver=true //:node \
     //:py_main \
     //:cli \
@@ -29,30 +28,18 @@ if [ $? -ne 0 ]; then
     exit
 fi
 
-BASE_DIR=`ls -l | grep bazel-bin | awk '{print $11}'`
-
-if [ ! -d "$BASE_DIR" ]; then
-    echo "BASE_DIR IS NULL"
-    exit
-fi
-
-key_word="ARG TARGET_PATH="
-row_num=$(sed -n "/${key_word}/=" Dockerfile.local)
-sed -i "${row_num}s#.*#ARG TARGET_PATH="${BASE_DIR}"#" Dockerfile.local
-
-rm -rf $BASE_DIR/python $BASE_DIR/config $BASE_DIR/example
-rm -f $BASE_DIR/Dockerfile.local
-rm -f $BASE_DIR/.dockerignore
-rm -rf $BASE_DIR/data
-
-cp -r ./example $BASE_DIR/
-cp -r ./data $BASE_DIR/
-cp -r ./python $BASE_DIR/
-cp -r ./config $BASE_DIR/
-cp ./Dockerfile.local $BASE_DIR/
-cp -r ./src $BASE_DIR/
-
-cd $BASE_DIR
-find ./ -name "_objs" > .dockerignore
+git rev-parse --abbrev-ref HEAD >> commit.txt
+git rev-parse HEAD >> commit.txt
+
+tar zcf bazel-bin.tar.gz bazel-bin/cli \
+        bazel-bin/node \
+        bazel-bin/py_main \
+        bazel-bin/src/primihub/pybind_warpper/opt_paillier_c2py.so \
+        bazel-bin/src/primihub/pybind_warpper/linkcontext.so \
+        python \
+        config \
+        example \
+        data \
+        commit.txt
 
 docker build -t $IMAGE_NAME:$TAG . -f Dockerfile.local
\ No newline at end of file

config/node0.yaml

@@ -52,8 +52,8 @@ datasets:
     model: "csv"
     source: "data/falcon/dataset/MNIST/input_1"
 
-  # FL fake dataset for hfl server
-  - description: "hfl_server_fake_data"
+  # FL fake dataset
+  - description: "fl_fake_data"
     model: "csv"
     source: "data/FL/fake.csv"
 

config/node1.yaml

@@ -75,6 +75,12 @@ datasets:
     source: "data/server_e.db3"
 
   # FL binclass
+  - description: "binclass_vfl_train_host"
+    model: "csv"
+    source: "data/FL/binclass/vfl/train/host.csv"
+  - description: "binclass_vfl_test_host"
+    model: "csv"
+    source: "data/FL/binclass/vfl/test/host.csv"
   - description: "binclass_hfl_train_client1"
     model: "csv"
     source: "data/FL/binclass/hfl/train/client1.csv"
@@ -83,6 +89,12 @@ datasets:
     source: "data/FL/binclass/hfl/test/client1.csv"
 
   # FL multiclass
+  - description: "multiclass_vfl_train_host"
+    model: "csv"
+    source: "data/FL/multiclass/vfl/train/host.csv"
+  - description: "multiclass_vfl_test_host"
+    model: "csv"
+    source: "data/FL/multiclass/vfl/test/host.csv"
   - description: "multiclass_hfl_train_client1"
     model: "csv"
     source: "data/FL/multiclass/hfl/train/client1.csv"

config/node2.yaml

@@ -54,6 +54,12 @@ datasets:
     source: "data/falcon/dataset/MNIST/input_0"
 
   # FL binclass
+  - description: "binclass_vfl_train_guest"
+    model: "csv"
+    source: "data/FL/binclass/vfl/train/guest.csv"
+  - description: "binclass_vfl_test_guest"
+    model: "csv"
+    source: "data/FL/binclass/vfl/test/guest.csv"
   - description: "binclass_hfl_train_client2"
     model: "csv"
     source: "data/FL/binclass/hfl/train/client2.csv"
@@ -62,6 +68,12 @@ datasets:
     source: "data/FL/binclass/hfl/test/client2.csv"
 
   # FL multiclass
+  - description: "multiclass_vfl_train_guest"
+    model: "csv"
+    source: "data/FL/multiclass/vfl/train/guest.csv"
+  - description: "multiclass_vfl_test_guest"
+    model: "csv"
+    source: "data/FL/multiclass/vfl/test/guest.csv"
   - description: "multiclass_hfl_train_client2"
     model: "csv"
     source: "data/FL/multiclass/hfl/train/client2.csv"

config/primihub_node0.yaml

@@ -34,8 +34,8 @@ datasets:
     model: "csv"
     source: "data/falcon/dataset/MNIST/input_1"
 
-  # FL fake dataset for hfl server
-  - description: "hfl_server_fake_data"
+  # FL fake dataset
+  - description: "fl_fake_data"
     model: "csv"
     source: "data/FL/fake.csv"
 

config/primihub_node1.yaml

@@ -55,6 +55,12 @@ datasets:
     source: "data/server_e.db3"
 
   # FL binclass
+  - description: "binclass_vfl_train_host"
+    model: "csv"
+    source: "data/FL/binclass/vfl/train/host.csv"
+  - description: "binclass_vfl_test_host"
+    model: "csv"
+    source: "data/FL/binclass/vfl/test/host.csv"
   - description: "binclass_hfl_train_client1"
     model: "csv"
     source: "data/FL/binclass/hfl/train/client1.csv"
@@ -63,6 +69,12 @@ datasets:
     source: "data/FL/binclass/hfl/test/client1.csv"
 
   # FL multiclass
+  - description: "multiclass_vfl_train_host"
+    model: "csv"
+    source: "data/FL/multiclass/vfl/train/host.csv"
+  - description: "multiclass_vfl_test_host"
+    model: "csv"
+    source: "data/FL/multiclass/vfl/test/host.csv"
   - description: "multiclass_hfl_train_client1"
     model: "csv"
     source: "data/FL/multiclass/hfl/train/client1.csv"

config/primihub_node2.yaml

@@ -35,6 +35,12 @@ datasets:
     source: "data/falcon/dataset/MNIST/input_0"
 
   # FL binclass
+  - description: "binclass_vfl_train_guest"
+    model: "csv"
+    source: "data/FL/binclass/vfl/train/guest.csv"
+  - description: "binclass_vfl_test_guest"
+    model: "csv"
+    source: "data/FL/binclass/vfl/test/guest.csv"
   - description: "binclass_hfl_train_client2"
     model: "csv"
     source: "data/FL/binclass/hfl/train/client2.csv"
@@ -43,6 +49,12 @@ datasets:
     source: "data/FL/binclass/hfl/test/client2.csv"
 
   # FL multiclass
+  - description: "multiclass_vfl_train_guest"
+    model: "csv"
+    source: "data/FL/multiclass/vfl/train/guest.csv"
+  - description: "multiclass_vfl_test_guest"
+    model: "csv"
+    source: "data/FL/multiclass/vfl/test/guest.csv"
   - description: "multiclass_hfl_train_client2"
     model: "csv"
     source: "data/FL/multiclass/hfl/train/client2.csv"

data/FL/binclass/vfl/test/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
+231,11.32,27.08,71.76,395.7,0.06883,0.03813,0.01633,0.003125,0.1869,0.05628,0.121,0.8927,1.059,8.605
+110,9.777,16.99,62.5,290.2,0.1037,0.08404,0.04334,0.01778,0.1584,0.07065,0.403,1.424,2.747,22.87
+327,12.03,17.93,76.09,446.0,0.07683,0.03892,0.001546,0.005592,0.1382,0.0607,0.2335,0.9097,1.466,16.97
+374,13.69,16.07,87.84,579.1,0.08302,0.06374,0.02556,0.02031,0.1872,0.05669,0.1705,0.5066,1.372,14.0
+511,14.81,14.7,94.66,680.7,0.08472,0.05016,0.03416,0.02541,0.1659,0.05348,0.2182,0.6232,1.677,20.72
+259,15.53,33.56,103.7,744.9,0.1063,0.1639,0.1751,0.08399,0.2091,0.0665,0.2419,1.278,1.903,23.02
+514,15.05,19.07,97.26,701.9,0.09215,0.08597,0.07486,0.04335,0.1561,0.05915,0.386,1.198,2.63,38.49
+201,17.54,19.32,115.1,951.6,0.08968,0.1198,0.1036,0.07488,0.1506,0.05491,0.3971,0.8282,3.088,40.73
+528,13.94,13.17,90.31,594.2,0.1248,0.09755,0.101,0.06615,0.1976,0.06457,0.5461,2.635,4.091,44.74
+390,10.26,12.22,65.75,321.6,0.09996,0.07542,0.01923,0.01968,0.18,0.06569,0.1911,0.5477,1.348,11.88
+28,15.3,25.27,102.4,732.4,0.1082,0.1697,0.1683,0.08751,0.1926,0.0654,0.439,1.012,3.498,43.5
+346,12.06,18.9,76.66,445.3,0.08386,0.05794,0.00751,0.008488,0.1555,0.06048,0.243,1.152,1.559,18.02
+206,9.876,17.27,62.92,295.4,0.1089,0.07232,0.01756,0.01952,0.1934,0.06285,0.2137,1.342,1.517,12.33
+428,11.13,16.62,70.47,381.1,0.08151,0.03834,0.01369,0.0137,0.1511,0.06148,0.1415,0.9671,0.968,9.704
+277,18.81,19.98,120.9,1102.0,0.08923,0.05884,0.0802,0.05843,0.155,0.04996,0.3283,0.828,2.363,36.74
+224,13.27,17.02,84.55,546.4,0.08445,0.04994,0.03554,0.02456,0.1496,0.05674,0.2927,0.8907,2.044,24.68
+443,10.57,18.32,66.82,340.9,0.08142,0.04462,0.01993,0.01111,0.2372,0.05768,0.1818,2.542,1.277,13.12
+11,15.78,17.89,103.6,781.0,0.0971,0.1292,0.09954,0.06606,0.1842,0.06082,0.5058,0.9849,3.564,54.16
+56,19.21,18.57,125.5,1152.0,0.1053,0.1267,0.1323,0.08994,0.1917,0.05961,0.7275,1.193,4.837,102.5
+497,12.47,17.31,80.45,480.1,0.08928,0.0763,0.03609,0.02369,0.1526,0.06046,0.1532,0.781,1.253,11.91
+345,10.26,14.71,66.2,321.6,0.09882,0.09159,0.03581,0.02037,0.1633,0.07005,0.338,2.509,2.394,19.33
+4,20.29,14.34,135.1,1297.0,0.1003,0.1328,0.198,0.1043,0.1809,0.05883,0.7572,0.7813,5.438,94.44
+99,14.42,19.77,94.48,642.5,0.09752,0.1141,0.09388,0.05839,0.1879,0.0639,0.2895,1.851,2.376,26.85
+86,14.48,21.46,94.25,648.2,0.09444,0.09947,0.1204,0.04938,0.2075,0.05636,0.4204,2.22,3.301,38.87
+122,24.25,20.2,166.2,1761.0,0.1447,0.2867,0.4268,0.2012,0.2655,0.06877,1.509,3.12,9.807,233.0
+145,11.9,14.65,78.11,432.8,0.1152,0.1296,0.0371,0.03003,0.1995,0.07839,0.3962,0.6538,3.021,25.03
+401,11.93,10.91,76.14,442.7,0.08872,0.05242,0.02606,0.01796,0.1601,0.05541,0.2522,1.045,1.649,18.95
+409,12.27,17.92,78.41,466.1,0.08685,0.06526,0.03211,0.02653,0.1966,0.05597,0.3342,1.781,2.079,25.79
+338,10.05,17.53,64.41,310.8,0.1007,0.07326,0.02511,0.01775,0.189,0.06331,0.2619,2.015,1.778,16.85
+15,14.54,27.54,96.73,658.8,0.1139,0.1595,0.1639,0.07364,0.2303,0.07077,0.37,1.033,2.879,32.55
+71,8.888,14.64,58.79,244.0,0.09783,0.1531,0.08606,0.02872,0.1902,0.0898,0.5262,0.8522,3.168,25.44
+119,17.95,20.01,114.2,982.0,0.08402,0.06722,0.07293,0.05596,0.2129,0.05025,0.5506,1.214,3.357,54.04
+458,13.0,25.13,82.61,520.2,0.08369,0.05073,0.01206,0.01762,0.1667,0.05449,0.2621,1.232,1.657,21.19
+51,13.64,16.34,87.21,571.8,0.07685,0.06059,0.01857,0.01723,0.1353,0.05953,0.1872,0.9234,1.449,14.55
+257,15.32,17.27,103.2,713.3,0.1335,0.2284,0.2448,0.1242,0.2398,0.07596,0.6592,1.059,4.061,59.46
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data/FL/binclass/vfl/test/host.csv

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data/FL/fake.csv

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\ No newline at end of file

data/FL/multiclass/vfl/test/guest.csv

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example/FL/logistic_regression/hfl_binclass_dpsgd.json

@@ -19,8 +19,8 @@
             "noise_multiplier": 2.0,
             "l2_norm_clip": 1.0,
             "secure_mode": true,
-            "learning_rate": "optimal",
-            "alpha": 0.0001,
+            "learning_rate": 1e-0,
+            "alpha": 1e-4,
             "batch_size": 100,
             "global_epoch": 10,
             "local_epoch": 1,
@@ -40,7 +40,7 @@
                 "model_path": "data/result/Charlie_model.pkl"
             },
             "Alice": {
-                "data_set": "hfl_server_fake_data"
+                "data_set": "fl_fake_data"
             }
         }
     }

example/FL/logistic_regression/hfl_binclass_paillier.json

@@ -16,8 +16,8 @@
             "process": "train",
             "task_name": "HFL_logistic_regression_binclass_paillier_train",
             "n_length": 2048,
-            "learning_rate": "optimal",
-            "alpha": 0.01,
+            "learning_rate": 1e-0,
+            "alpha": 1e-4,
             "batch_size": 100,
             "global_epoch": 10,
             "local_epoch": 1,
@@ -37,7 +37,7 @@
                 "model_path": "data/result/Charlie_model.pkl"
             },
             "Alice": {
-                "data_set": "hfl_server_fake_data"
+                "data_set": "fl_fake_data"
             }
         }
     }

example/FL/logistic_regression/hfl_binclass_plaintext.json

@@ -15,8 +15,8 @@
             "method": "Plaintext",
             "process": "train",
             "task_name": "HFL_logistic_regression_binclass_plaintext_train",
-            "learning_rate": "optimal",
-            "alpha": 0.0001,
+            "learning_rate": 1e-0,
+            "alpha": 1e-4,
             "batch_size": 100,
             "global_epoch": 10,
             "local_epoch": 1,
@@ -36,7 +36,7 @@
                 "model_path": "data/result/Charlie_model.pkl"
             },
             "Alice": {
-                "data_set": "hfl_server_fake_data"
+                "data_set": "fl_fake_data"
             }
         }
     }

example/FL/logistic_regression/hfl_multiclass_dpsgd.json

@@ -19,8 +19,8 @@
             "noise_multiplier": 2.0,
             "l2_norm_clip": 1.0,
             "secure_mode": true,
-            "learning_rate": "optimal",
-            "alpha": 0.0005,
+            "learning_rate": 1e-1,
+            "alpha": 1e-4,
             "batch_size": 100,
             "global_epoch": 10,
             "local_epoch": 1,
@@ -40,7 +40,7 @@
                 "model_path": "data/result/Charlie_model.pkl"
             },
             "Alice": {
-                "data_set": "hfl_server_fake_data"
+                "data_set": "fl_fake_data"
             }
         }
     }

example/FL/logistic_regression/hfl_multiclass_plaintext.json

@@ -15,8 +15,8 @@
             "method": "Plaintext",
             "process": "train",
             "task_name": "HFL_logistic_regression_multiclass_plaintext_train",
-            "learning_rate": "optimal",
-            "alpha": 0.005,
+            "learning_rate": 1e-1,
+            "alpha": 1e-4,
             "batch_size": 100,
             "global_epoch": 10,
             "local_epoch": 1,
@@ -36,7 +36,7 @@
                 "model_path": "data/result/Charlie_model.pkl"
             },
             "Alice": {
-                "data_set": "hfl_server_fake_data"
+                "data_set": "fl_fake_data"
             }
         }
     }

example/FL/logistic_regression/hetero_lr_infer.json → example/FL/logistic_regression/vfl_binclass_plaintext.json

@@ -4,33 +4,37 @@
     },
     "component_params": {
         "roles": {
+            "host": "Bob",
             "guest": [
                 "Charlie"
-            ],
-            "host": [
-                "Bob"
             ]
         },
         "common_params": {
-            "model": "HeteroLRInfer",
-            "task_name": "predict",
-            "metric_path": "data/result/metrics.json",
-            "model_pred": "data/result/pred.csv"
+            "model": "VFL_logistic_regression",
+            "method": "Plaintext",
+            "process": "train",
+            "task_name": "VFL_logistic_regression_binclass_plaintext_train",
+            "learning_rate": 1e-0,
+            "alpha": 1e-4,
+            "epoch": 10,
+            "shuffle_seed": 0,
+            "batch_size": 100,
+            "print_metrics": true
         },
         "role_params": {
             "Bob": {
-                "data_set": "test_hetero_xgb_host",
-                "id": "id",
+                "data_set": "binclass_vfl_train_host",
                 "selected_column": null,
+                "id": "id",
                 "label": "y",
-                "model_path": "data/result/host_model.pkl"
+                "model_path": "data/result/host_model.pkl",
+                "metric_path": "data/result/metrics.json"
             },
             "Charlie": {
-                "data_set": "test_hetero_xgb_guest",
-                "id": "id",
-                "model_path": "data/result/guest_model.pkl",
+                "data_set": "binclass_vfl_train_guest",
                 "selected_column": null,
-                "label": null
+                "id": "id",
+                "model_path": "data/result/guest_model.pkl"
             }
         }
     }

example/FL/logistic_regression/vfl_binclass_predict.json

+{
+    "party_info": {
+        "task_manager": "127.0.0.1:50050"
+    },
+    "component_params": {
+        "roles": {
+            "host": "Bob",
+            "guest": [
+                "Charlie"
+            ]
+        },
+        "common_params": {
+            "model": "VFL_logistic_regression",
+            "process": "predict",
+            "task_name": "VFL_logistic_regression_binclass_predict"
+        },
+        "role_params": {
+            "Bob": {
+                "data_set": "binclass_vfl_test_host",
+                "model_path": "data/result/host_model.pkl",
+                "predict_path": "data/result/host_predict.csv"
+            },
+            "Charlie": {
+                "data_set": "binclass_vfl_test_guest",
+                "model_path": "data/result/guest_model.pkl"
+            }
+        }
+    }
+}
\ No newline at end of file

example/FL/logistic_regression/hetero_lr.json → example/FL/logistic_regression/vfl_multiclass_plaintext.json

@@ -4,49 +4,37 @@
     },
     "component_params": {
         "roles": {
+            "host": "Bob",
             "guest": [
                 "Charlie"
-            ],
-            "host": [
-                "Bob"
             ]
         },
         "common_params": {
-            "model": "HeteroLR",
-            "task_name": "train",
-            "learning_rate": 0.01,
-            "alpha": 0.0001,
-            "epochs": 50,
-            "penalty": "l2",
-            "optimal_method": "momentum",
-            "momentum": 0.7,
-            "random_state": 2023,
-            "scale_type": "z-score",
-            "batch_size": 512,
-            "sample_method": "random",
-            "sample_ratio": 0.3,
-            "loss_type": "log",
-            "prev_grad": 0,
-            "metric_path": "data/result/metrics.json",
-            "model_pred": "data/result/pred.csv"
+            "model": "VFL_logistic_regression",
+            "method": "Plaintext",
+            "process": "train",
+            "task_name": "VFL_logistic_regression_multiclass_plaintext_train",
+            "learning_rate": 1e-0,
+            "alpha": 1e-4,
+            "epoch": 10,
+            "shuffle_seed": 0,
+            "batch_size": 100,
+            "print_metrics": true
         },
         "role_params": {
             "Bob": {
-                "data_set": "train_hetero_xgb_host",
-                "id": "id",
+                "data_set": "multiclass_vfl_train_host",
                 "selected_column": null,
-                "add_noise": "regular",
-                "tol": 0.001,
+                "id": "id",
                 "label": "y",
                 "model_path": "data/result/host_model.pkl",
-                "n_iter_no_change": 5
+                "metric_path": "data/result/metrics.json"
             },
             "Charlie": {
-                "data_set": "train_hetero_xgb_guest",
-                "id": "id",
-                "model_path": "data/result/guest_model.pkl",
+                "data_set": "multiclass_vfl_train_guest",
                 "selected_column": null,
-                "label": null
+                "id": "id",
+                "model_path": "data/result/guest_model.pkl"
             }
         }
     }

example/FL/logistic_regression/vfl_multiclass_predict.json

+{
+    "party_info": {
+        "task_manager": "127.0.0.1:50050"
+    },
+    "component_params": {
+        "roles": {
+            "host": "Bob",
+            "guest": [
+                "Charlie"
+            ]
+        },
+        "common_params": {
+            "model": "VFL_logistic_regression",
+            "process": "predict",
+            "task_name": "VFL_logistic_regression_multiclass_predict"
+        },
+        "role_params": {
+            "Bob": {
+                "data_set": "multiclass_vfl_test_host",
+                "model_path": "data/result/host_model.pkl",
+                "predict_path": "data/result/host_predict.csv"
+            },
+            "Charlie": {
+                "data_set": "multiclass_vfl_test_guest",
+                "model_path": "data/result/guest_model.pkl"
+            }
+        }
+    }
+}
\ No newline at end of file

example/FL/neural_network/hfl_binclass_dpsgd.json

@@ -41,7 +41,7 @@
                 "model_path": "data/result/Charlie_model.pkl"
             },
             "Alice": {
-                "data_set": "hfl_server_fake_data"
+                "data_set": "fl_fake_data"
             }
         }
     }

example/FL/neural_network/hfl_binclass_plaintext.json

@@ -38,7 +38,7 @@
                 "model_path": "data/result/Charlie_model.pkl"
             },
             "Alice": {
-                "data_set": "hfl_server_fake_data"
+                "data_set": "fl_fake_data"
             }
         }
     }

example/FL/neural_network/hfl_cnn_multiclass_dpsgd.json

@@ -37,7 +37,7 @@
                 "model_path": "data/result/Charlie_model.pkl"
             },
             "Alice": {
-                "data_set": "hfl_server_fake_data"
+                "data_set": "fl_fake_data"
             }
         }
     }

example/FL/neural_network/hfl_cnn_multiclass_plaintext.json

@@ -34,7 +34,7 @@
                 "model_path": "data/result/Charlie_model.pkl"
             },
             "Alice": {
-                "data_set": "hfl_server_fake_data"
+                "data_set": "fl_fake_data"
             }
         }
     }

example/FL/neural_network/hfl_multiclass_dpsgd.json

@@ -41,7 +41,7 @@
                 "model_path": "data/result/Charlie_model.pkl"
             },
             "Alice": {
-                "data_set": "hfl_server_fake_data"
+                "data_set": "fl_fake_data"
             }
         }
     }

example/FL/neural_network/hfl_multiclass_plaintext.json

@@ -38,7 +38,7 @@
                 "model_path": "data/result/Charlie_model.pkl"
             },
             "Alice": {
-                "data_set": "hfl_server_fake_data"
+                "data_set": "fl_fake_data"
             }
         }
     }

example/FL/neural_network/hfl_regression_dpsgd.json

@@ -41,7 +41,7 @@
                 "model_path": "data/result/Charlie_model.pkl"
             },
             "Alice": {
-                "data_set": "hfl_server_fake_data"
+                "data_set": "fl_fake_data"
             }
         }
     }

example/FL/neural_network/hfl_regression_plaintext.json

@@ -38,7 +38,7 @@
                 "model_path": "data/result/Charlie_model.pkl"
             },
             "Alice": {
-                "data_set": "hfl_server_fake_data"
+                "data_set": "fl_fake_data"
             }
         }
     }

python/primihub/FL/logistic_regression/base.py

 import numpy as np
-import logging
+from primihub.utils.logger_util import logger
 
 
 class LogisticRegression:
 
     # l2 regularization by default, alpha is the penalty parameter
     def __init__(self, x, y, 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
+        self.alpha = alpha
 
-        if max(y) == 1:
+        max_y = max(y)
+        if max_y == 1:
             self.theta = np.zeros(x.shape[1] + 1)
             self.multiclass = False
         else:
-            self.theta = np.zeros((x.shape[1] + 1, max(y) + 1))
+            self.theta = np.zeros((x.shape[1] + 1, max_y + 1))
             self.multiclass = 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)
-        
-        if self.learning_rate == 'optimal':
-            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)
 
     def sigmoid(self, x):
         return 1.0 / (1.0 + np.exp(-x))
@@ -80,19 +65,9 @@ class LogisticRegression:
     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):
-        # optimal learning rate: 1.0 / (alpha * (t0 + t))
-        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 fit(self, x, y):
-        if self.learning_rate == 'optimal':
-            self.gradient_descent_olr(x, y)
-        else:
-            self.gradient_descent(x, y)
+        self.gradient_descent(x, y)
 
     def predict_prob(self, x):
         z = x.dot(self.theta[1:]) + self.theta[0]
@@ -198,7 +173,7 @@ class LogisticRegression_Paillier(LogisticRegression, PaillierFunc):
 
     def compute_grad(self, x, y):
         if self.multiclass:
-            logging.error("Paillier method doesn't support multiclass classification")
+            logger.error("Paillier method doesn't support multiclass classification")
         else:
             # Approximate gradient
             # First order of taylor expansion: sigmoid(x) = 0.5 + 0.25 * (x.dot(w) + b)
@@ -212,4 +187,4 @@ class LogisticRegression_Paillier(LogisticRegression, PaillierFunc):
         return (0.5 - y).dot(x.dot(self.theta[1:] + self.theta[0])) / x.shape[0]
 
     def CELoss(self, x, y, eps=1e-20):
-        logging.error("Paillier method doesn't support multiclass classification")
+        logger.error("Paillier method doesn't support multiclass classification")

python/primihub/FL/logistic_regression/hetero_lr_infer.py

-import pickle
-import json
-import pandas as pd
-import numpy as np
-from sklearn import metrics
-from primihub.FL.utils.net_work import GrpcClient
-from primihub.utils.evaluation import evaluate_ks_and_roc_auc, plot_lift_and_gain, eval_acc
-from primihub.FL.utils.base import BaseModel
-from primihub.FL.utils.dataset import read_csv
-from primihub.FL.utils.file import check_directory_exist
-
-
-class HeteroLrHostInfer(BaseModel):
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self.set_inputs()
-        remote_party = self.roles[self.role_params['others_role']][0]
-        self.channel = GrpcClient(local_party=self.role_params['self_name'],
-                                    remote_party=remote_party,
-                                    node_info=self.node_info,
-                                    task_info=self.task_info)
-
-    def set_inputs(self):
-        # set common params
-        self.model = self.common_params['model']
-        self.task_name = self.common_params['task_name']
-        self.metric_path = self.common_params['metric_path']
-        self.model_pred = self.common_params['model_pred']
-
-        # set role params
-        self.data_set = self.role_params['data_set']
-        self.id = self.role_params['id']
-        self.selected_column = self.role_params['selected_column']
-        self.label = self.role_params['label']
-        self.model_path = self.role_params['model_path']
-
-        # read from data path
-        data_path = self.role_params['data']['data_path']
-        self.data = read_csv(data_path, selected_column=None, id=None)
-
-    def load_dict(self):
-        with open(self.model_path, "rb") as current_model:
-            model_dict = pickle.load(current_model)
-
-        self.weights = model_dict['weights']
-        self.bias = model_dict['bias']
-        self.col_names = model_dict['columns']
-        self.std = model_dict['std']
-
-    def preprocess(self):
-        if self.id in self.data.columns:
-            self.data.pop(self.id)
-
-        if self.label in self.data.columns:
-            self.y = self.data.pop(self.label).values
-
-        if len(self.col_names) > 0:
-            self.data = self.data[self.col_names].values
-
-        if self.std is not None:
-            self.data = self.std.transform(self.data)
-
-    def predict_raw(self, x):
-        host_part = np.dot(x, self.weights) + self.bias
-        guest_part = self.channel.recv("guest_part")
-        h = host_part + guest_part
-
-        return h
-
-    def sigmoid(self, x):
-        return 1.0 / (1 + np.exp(-x))
-
-    def run(self):
-        origin_data = self.data.copy()
-        self.load_dict()
-        self.preprocess()
-        y_hat = self.predict_raw(self.data)
-        pred_prob = self.sigmoid(y_hat)
-        pred_y = (pred_prob > 0.5).astype('int')
-
-        pred_df = pd.DataFrame({'pred_prob': pred_prob,
-                                'pred_y': pred_y})
-        data_result = pd.concat([origin_data, pred_df], axis=1)
-        check_directory_exist(self.model_pred)
-        data_result.to_csv(self.model_pred, index=False)
-
-        # if self.label is not None:
-        #     acc = sum((pred_y == self.y).astype('int')) / self.data.shape[0]
-        #     ks, auc = evaluate_ks_and_roc_auc(self.y, self.sigmoid(y_hat))
-        #     fpr, tpr, threshold = metrics.roc_curve(self.y, self.sigmoid(y_hat))
-
-        #     evals = {
-        #         "test_acc": acc,
-        #         "test_ks": ks,
-        #         "test_auc": auc,
-        #         "test_fpr": fpr.tolist(),
-        #         "test_tpr": tpr.tolist()
-        #     }
-
-        #     metrics_buff = json.dumps(evals)
-
-        #     check_directory_exist(self.metric_path)
-        #     with open(self.metric_path, 'w') as filePath:
-        #         filePath.write(metrics_buff)
-        #     print("test acc is", evals)
-
-    def get_summary(self):
-        return {}
-
-    def get_outputs(self):
-        return {}
-
-    def get_status(self):
-        return {}
-
-
-class HeteroLrGuestInfer(BaseModel):
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self.set_inputs()
-        remote_party = self.roles[self.role_params['others_role']][0]
-        self.channel = GrpcClient(local_party=self.role_params['self_name'],
-                                    remote_party=remote_party,
-                                    node_info=self.node_info,
-                                    task_info=self.task_info)
-
-    def set_inputs(self):
-        self.model = self.common_params['model']
-        self.task_name = self.common_params['task_name']
-
-        # set role params
-        self.data_set = self.role_params['data_set']
-        self.id = self.role_params['id']
-        self.selected_column = self.role_params['selected_column']
-        self.label = self.role_params['label']
-        self.model_path = self.role_params['model_path']
-
-        # read from data path
-        data_path = self.role_params['data']['data_path']
-        self.data = read_csv(data_path, selected_column=None, id=None)
-
-    def load_dict(self):
-        with open(self.model_path, "rb") as current_model:
-            model_dict = pickle.load(current_model)
-
-        self.weights = model_dict['weights']
-        self.bias = model_dict['bias']
-        self.col_names = model_dict['columns']
-        self.std = model_dict['std']
-
-    def preprocess(self):
-        if self.id in self.data.columns:
-            self.data.pop(self.id)
-
-        if self.label in self.data.columns:
-            self.y = self.data.pop(self.label).values
-
-        if len(self.col_names) > 0:
-            self.data = self.data[self.col_names].values
-
-        if self.std is not None:
-            self.data = self.std.transform(self.data)
-
-    def predict_raw(self, x):
-        guest_part = np.dot(x, self.weights) + self.bias
-        self.channel.send("guest_part", guest_part)
-
-    def run(self):
-        self.load_dict()
-        self.preprocess()
-        self.predict_raw(self.data)
-
-    def get_summary(self):
-        return {}
-
-    def get_outputs(self):
-        return {}
-
-    def get_status(self):
-        return {}
\ No newline at end of file

python/primihub/FL/logistic_regression/hfl_client.py

 from primihub.FL.utils.net_work import GrpcClient
 from primihub.FL.utils.base import BaseModel
 from primihub.FL.utils.file import check_directory_exist
-from primihub.FL.utils.dataset import read_csv,\
+from primihub.FL.utils.dataset import read_data,\
                                       DataLoader,\
                                       DPDataLoader
 from primihub.utils.logger_util import logger
@@ -19,6 +19,7 @@ from .base import LogisticRegression,\
 
 
 class LogisticRegressionClient(BaseModel):
+    
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         
@@ -37,10 +38,11 @@ class LogisticRegressionClient(BaseModel):
                                     task_info=self.task_info)
 
         # load dataset
-        data_path = self.role_params['data']['data_path']
         selected_column = self.common_params['selected_column']
         id = self.common_params['id']
-        x = read_csv(data_path, selected_column, id)
+        x = read_data(data_info=self.role_params['data'],
+                      selected_column=selected_column,
+                      id=id)
         label = self.common_params['label']
         y = x.pop(label).values
         x = x.values
@@ -66,7 +68,9 @@ class LogisticRegressionClient(BaseModel):
                                      self.common_params['alpha'],
                                      server_channel)
         else:
-            logger.error(f"Not supported method: {method}")
+            error_msg = f"Unsupported method: {method}"
+            logger.error(error_msg)
+            raise RuntimeError(error_msg)
 
         # data preprocessing
         # minmaxscaler
@@ -148,8 +152,7 @@ class LogisticRegressionClient(BaseModel):
             modelFile = pickle.load(file_path)
 
         # load dataset
-        data_path = self.role_params['data']['data_path']
-        origin_data = read_csv(data_path, selected_column=None, id=None)
+        origin_data = read_data(data_info=self.role_params['data'])
 
         x = origin_data.copy()
         selected_column = modelFile['selected_column']

python/primihub/FL/logistic_regression/hfl_server.py

@@ -13,6 +13,7 @@ from .base import PaillierFunc
 
 
 class LogisticRegressionServer(BaseModel):
+    
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         
@@ -38,7 +39,9 @@ class LogisticRegressionServer(BaseModel):
                                      self.common_params['n_length'],
                                      client_channel)
         else:
-            logger.error(f"Not supported method: {method}")
+            error_msg = f"Unsupported method: {method}"
+            logger.error(error_msg)
+            raise RuntimeError(error_msg)
 
         # data preprocessing
         # minmaxscaler
@@ -146,8 +149,10 @@ class Plaintext_DPSGD_Server:
         metrics_name = metrics_name.lower()
         supported_metrics = ['loss', 'acc', 'auc']
         if metrics_name not in supported_metrics:
-            logger.error(f"""Not supported metrics {metrics_name},
-                          use {supported_metrics} instead""")
+            error_msg = f"""Unsupported metrics {metrics_name},
+                          use {supported_metrics} instead"""
+            logger.error(error_msg)
+            raise RuntimeError(error_msg)
 
         client_metrics = self.client_channel.recv_all(metrics_name)
             

python/primihub/FL/logistic_regression/vfl_base.py

+import numpy as np
+from primihub.utils.logger_util import logger
+from .base import LogisticRegression
+
+
+class LogisticRegression_Host_Plaintext(LogisticRegression):
+
+    def __init__(self, x, y, learning_rate=0.2, alpha=0.0001):
+
+        super().__init__(x, y, learning_rate, alpha)
+
+    def compute_z(self, x, guest_z):
+        z = x.dot(self.theta[1:]) + self.theta[0]
+        z += np.array(guest_z).sum(axis=0)
+        return z
+    
+    def predict_prob(self, z):
+        if self.multiclass:
+            return self.softmax(z)
+        else:
+            return self.sigmoid(z)
+    
+    def compute_error(self, y, z):
+        if self.multiclass:
+            error = self.predict_prob(z)
+            idx = np.arange(len(y))
+            error[idx, y] -= 1
+        else:
+            error = self.predict_prob(z) - y
+        return error
+    
+    def compute_regular_loss(self, guest_regular_loss):
+        return 0.5 * self.alpha * (self.theta ** 2).sum() \
+               + sum(guest_regular_loss)
+
+    def BCELoss(self, y, z, regular_loss):
+        return (np.maximum(z, 0.).sum() - y.dot(z) +
+                np.log1p(np.exp(-np.abs(z))).sum()) / z.shape[0] \
+                + regular_loss
+
+    def CELoss(self, y, z, regular_loss, eps=1e-20):
+        prob = self.predict_prob(z)
+        return -np.sum(np.log(np.clip(prob[np.arange(len(y)), y], eps, 1.))) \
+                / z.shape[0] + regular_loss
+    
+    def loss(self, y, z, regular_loss):
+        if self.multiclass:
+            return self.CELoss(y, z, regular_loss)
+        else:
+            return self.BCELoss(y, z, regular_loss)
+    
+    def compute_grad(self, x, error):
+        if self.multiclass:
+            return np.vstack((error.sum(axis=0, keepdims=True), x.T.dot(error))) \
+                    / x.shape[0] + self.alpha * self.theta
+        else:
+            return np.hstack((error.sum(keepdims=True), x.T.dot(error))) \
+                    / x.shape[0] + self.alpha * self.theta
+
+    def gradient_descent(self, x, error):
+        grad = self.compute_grad(x, error)
+        self.theta -= self.learning_rate * grad
+    
+    def fit(self, x, error):
+        self.gradient_descent(x, error)
+
+
+class LogisticRegression_Guest_Plaintext(LogisticRegression_Host_Plaintext):
+
+    def __init__(self, x, learning_rate=0.2, alpha=0.0001, output_dim=1):
+
+        self.learning_rate = learning_rate
+        self.alpha = alpha
+
+        if output_dim > 2:
+            self.theta = np.zeros((x.shape[1], output_dim))
+            self.multiclass = True
+        else:
+            self.theta = np.zeros(x.shape[1])
+            self.multiclass = False
+    
+    def compute_z(self, x):
+        return x.dot(self.theta)
+    
+    def compute_regular_loss(self):
+        return 0.5 * self.alpha * (self.theta ** 2).sum()
+    
+    def compute_grad(self, x, error):
+        return x.T.dot(error) / x.shape[0] + self.alpha * self.theta
\ No newline at end of file

python/primihub/FL/model_map.json

@@ -7,6 +7,10 @@
         "client": "primihub.FL.logistic_regression.hfl_client.LogisticRegressionClient",
         "server": "primihub.FL.logistic_regression.hfl_server.LogisticRegressionServer"
     },
+    "VFL_logistic_regression": {
+        "guest": "primihub.FL.logistic_regression.vfl_guest.LogisticRegressionGuest",
+        "host": "primihub.FL.logistic_regression.vfl_host.LogisticRegressionHost"
+    },
     "HFL_neural_network": {
         "client": "primihub.FL.neural_network.hfl_client.NeuralNetworkClient",
         "server": "primihub.FL.neural_network.hfl_server.NeuralNetworkServer"
@@ -27,14 +31,6 @@
         "guest": "primihub.FL.xgboost.gbt.VGBGuestInfer",
         "host": "primihub.FL.xgboost.gbt.VGBTHostInfer"
     },
-    "HeteroLR": {
-        "guest": "primihub.FL.logistic_regression.hetero_lr.HeteroLrGuest",
-        "host": "primihub.FL.logistic_regression.hetero_lr.HeteroLrHost"
-    },
-    "HeteroLRInfer": {
-        "guest": "primihub.FL.logistic_regression.hetero_lr_infer.HeteroLrGuestInfer",
-        "host": "primihub.FL.logistic_regression.hetero_lr_infer.HeteroLrHostInfer"
-    },
     "HeteroIV": {
         "guest": "primihub.FL.preprocessing.woe_iv.IVGuest",
         "host": "primihub.FL.preprocessing.woe_iv.IVHost"

python/primihub/FL/neural_network/base.py

@@ -7,12 +7,15 @@ from .cnn import NeuralNetwork as CNN
 
 def create_model(method, output_dim, device, nn_model='mlp'):
     # select model
+    nn_model = nn_model.lower()
     if nn_model == 'mlp':
         model = MLP(output_dim)
     elif nn_model == 'cnn':
         model = CNN(output_dim)
     else:
-        logger.error(f"Unsupported NN model: {nn_model}")
+        error_msg = f"Unsupported NN model: {nn_model}"
+        logger.error(error_msg)
+        raise RuntimeError(error_msg)
 
     # validate model (DPSGD)
     if method == 'DPSGD':
@@ -25,6 +28,7 @@ def create_model(method, output_dim, device, nn_model='mlp'):
 
 
 def choose_loss_fn(output_dim, task):
+    task = task.lower()
     if task == 'classification':
         if output_dim == 1:
             return torch.nn.BCEWithLogitsLoss()
@@ -33,10 +37,13 @@ def choose_loss_fn(output_dim, task):
     if task == 'regression':
         return torch.nn.MSELoss()
     else:
-        logger.error(f"Not supported task: {task}")
+        error_msg = f"Unsupported task: {task}"
+        logger.error(error_msg)
+        raise RuntimeError(error_msg)
 
 
 def choose_optimizer(model, optimizer, learning_rate, alpha):
+    optimizer = optimizer.lower()
     if optimizer == 'adadelta':
         return torch.optim.Adadelta(model.parameters(),
                                     lr=learning_rate,
@@ -78,4 +85,6 @@ def choose_optimizer(model, optimizer, learning_rate, alpha):
                                lr=learning_rate,
                                weight_decay=alpha)
     else:
-        logger.error(f"Not supported optimizer: {optimizer}")
+        error_msg = f"Unsupported optimizer: {optimizer}"
\ No newline at end of file
+        logger.error(error_msg)
+        raise RuntimeError(error_msg)
\ No newline at end of file

python/primihub/FL/neural_network/hfl_cnn_server.py

@@ -41,7 +41,9 @@ class CNNServer(BaseModel):
                                   device,
                                   client_channel)
         else:
-            logger.error(f"Not supported method: {method}")
+            error_msg = f"Unsupported method: {method}"
+            logger.error(error_msg)
+            raise RuntimeError(error_msg)
 
         # model training
         logger.info("-------- start training --------")

python/primihub/FL/preprocessing/base.py

@@ -7,12 +7,16 @@ class PreprocessBase:
         if FL_type in ['V', 'H']:
             self.FL_type = FL_type
         else:
-            logger.error(f"Unsupported FL type: {FL_type}")
+            error_msg = f"Unsupported FL type: {FL_type}"
+            logger.error(error_msg)
+            raise RuntimeError(error_msg)
 
         if role in ['host', 'guest', 'client', 'server']:
             self.role = role
         else:
-            logger.error(f"Unsupported role: {role}")
+            error_msg = f"Unsupported role: {role}"
+            logger.error(error_msg)
+            raise RuntimeError(error_msg)
         
         self.channel = channel