forked from mindspore-Ecosystem/mindspore
191 lines
8.4 KiB
Python
191 lines
8.4 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import argparse
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import ast
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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from src.model import LeNet5
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from src.adam import AdamWeightDecayOp
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parser = argparse.ArgumentParser(description="test_fl_lenet")
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parser.add_argument("--device_target", type=str, default="CPU")
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parser.add_argument("--server_mode", type=str, default="FEDERATED_LEARNING")
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parser.add_argument("--ms_role", type=str, default="MS_WORKER")
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parser.add_argument("--worker_num", type=int, default=0)
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parser.add_argument("--server_num", type=int, default=1)
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parser.add_argument("--scheduler_ip", type=str, default="127.0.0.1")
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parser.add_argument("--scheduler_port", type=int, default=8113)
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parser.add_argument("--fl_server_port", type=int, default=6666)
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parser.add_argument("--start_fl_job_threshold", type=int, default=1)
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parser.add_argument("--start_fl_job_time_window", type=int, default=3000)
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parser.add_argument("--update_model_ratio", type=float, default=1.0)
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parser.add_argument("--update_model_time_window", type=int, default=3000)
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parser.add_argument("--fl_name", type=str, default="Lenet")
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parser.add_argument("--fl_iteration_num", type=int, default=25)
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parser.add_argument("--client_epoch_num", type=int, default=20)
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parser.add_argument("--client_batch_size", type=int, default=32)
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parser.add_argument("--client_learning_rate", type=float, default=0.1)
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parser.add_argument("--scheduler_manage_port", type=int, default=11202)
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parser.add_argument("--config_file_path", type=str, default="")
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parser.add_argument("--pki_verify", type=ast.literal_eval, default=False)
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# parameters used for pki_verify=True
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# You can download root_first_ca, root_second_ca and equip_crl
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# from https://pki.consumer.huawei.com/ca/
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parser.add_argument("--root_first_ca_path", type=str, default="")
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parser.add_argument("--root_second_ca_path", type=str, default="")
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parser.add_argument("--equip_crl_path", type=str, default="")
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parser.add_argument("--replay_attack_time_diff", type=int, default=600000)
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parser.add_argument("--encrypt_type", type=str, default="NOT_ENCRYPT")
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# parameters for encrypt_type='DP_ENCRYPT'
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parser.add_argument("--dp_eps", type=float, default=50.0)
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parser.add_argument("--dp_delta", type=float, default=0.01) # 1/worker_num
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parser.add_argument("--dp_norm_clip", type=float, default=1.0)
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# parameters for encrypt_type='PW_ENCRYPT'
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parser.add_argument("--share_secrets_ratio", type=float, default=1.0)
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parser.add_argument("--cipher_time_window", type=int, default=300000)
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parser.add_argument("--reconstruct_secrets_threshold", type=int, default=3)
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parser.add_argument("--client_password", type=str, default="")
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parser.add_argument("--server_password", type=str, default="")
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parser.add_argument("--enable_ssl", type=ast.literal_eval, default=False)
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# parameters for 'SIGNDS'
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parser.add_argument("--sign_k", type=float, default=0.01)
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parser.add_argument("--sign_eps", type=float, default=100)
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parser.add_argument("--sign_thr_ratio", type=float, default=0.6)
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parser.add_argument("--sign_global_lr", type=float, default=0.1)
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parser.add_argument("--sign_dim_out", type=int, default=0)
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parser.add_argument("--global_iteration_time_window", type=int, default=3600000)
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# parameters for "compression"
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parser.add_argument("--upload_compress_type", type=str, default="NO_COMPRESS",
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choices=["NO_COMPRESS", "DIFF_SPARSE_QUANT"])
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parser.add_argument("--upload_sparse_rate", type=float, default=0.5)
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parser.add_argument("--download_compress_type", type=str, default="NO_COMPRESS",
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choices=["NO_COMPRESS", "QUANT"])
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args, _ = parser.parse_known_args()
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device_target = args.device_target
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server_mode = args.server_mode
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ms_role = args.ms_role
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worker_num = args.worker_num
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server_num = args.server_num
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scheduler_ip = args.scheduler_ip
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scheduler_port = args.scheduler_port
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fl_server_port = args.fl_server_port
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start_fl_job_threshold = args.start_fl_job_threshold
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start_fl_job_time_window = args.start_fl_job_time_window
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update_model_ratio = args.update_model_ratio
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update_model_time_window = args.update_model_time_window
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share_secrets_ratio = args.share_secrets_ratio
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cipher_time_window = args.cipher_time_window
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reconstruct_secrets_threshold = args.reconstruct_secrets_threshold
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fl_name = args.fl_name
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fl_iteration_num = args.fl_iteration_num
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client_epoch_num = args.client_epoch_num
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client_batch_size = args.client_batch_size
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client_learning_rate = args.client_learning_rate
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scheduler_manage_port = args.scheduler_manage_port
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config_file_path = args.config_file_path
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dp_eps = args.dp_eps
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dp_delta = args.dp_delta
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dp_norm_clip = args.dp_norm_clip
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encrypt_type = args.encrypt_type
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pki_verify = args.pki_verify
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root_first_ca_path = args.root_first_ca_path
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root_second_ca_path = args.root_second_ca_path
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equip_crl_path = args.equip_crl_path
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replay_attack_time_diff = args.replay_attack_time_diff
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client_password = args.client_password
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server_password = args.server_password
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enable_ssl = args.enable_ssl
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sign_k = args.sign_k
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sign_eps = args.sign_eps
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sign_thr_ratio = args.sign_thr_ratio
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sign_global_lr = args.sign_global_lr
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sign_dim_out = args.sign_dim_out
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global_iteration_time_window = args.global_iteration_time_window
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upload_compress_type = args.upload_compress_type
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upload_sparse_rate = args.upload_sparse_rate
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download_compress_type = args.download_compress_type
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ctx = {
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"enable_fl": True,
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"server_mode": server_mode,
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"ms_role": ms_role,
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"worker_num": worker_num,
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"server_num": server_num,
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"scheduler_ip": scheduler_ip,
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"scheduler_port": scheduler_port,
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"fl_server_port": fl_server_port,
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"start_fl_job_threshold": start_fl_job_threshold,
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"start_fl_job_time_window": start_fl_job_time_window,
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"update_model_ratio": update_model_ratio,
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"update_model_time_window": update_model_time_window,
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"share_secrets_ratio": share_secrets_ratio,
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"cipher_time_window": cipher_time_window,
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"reconstruct_secrets_threshold": reconstruct_secrets_threshold,
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"fl_name": fl_name,
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"fl_iteration_num": fl_iteration_num,
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"client_epoch_num": client_epoch_num,
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"client_batch_size": client_batch_size,
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"client_learning_rate": client_learning_rate,
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"scheduler_manage_port": scheduler_manage_port,
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"config_file_path": config_file_path,
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"pki_verify": pki_verify,
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"root_first_ca_path": root_first_ca_path,
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"root_second_ca_path": root_second_ca_path,
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"equip_crl_path": equip_crl_path,
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"replay_attack_time_diff": replay_attack_time_diff,
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"dp_eps": dp_eps,
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"dp_delta": dp_delta,
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"dp_norm_clip": dp_norm_clip,
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"encrypt_type": encrypt_type,
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"client_password": client_password,
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"server_password": server_password,
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"enable_ssl": enable_ssl,
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"sign_k": sign_k,
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"sign_eps": sign_eps,
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"sign_thr_ratio": sign_thr_ratio,
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"sign_global_lr": sign_global_lr,
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"sign_dim_out": sign_dim_out,
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"global_iteration_time_window": global_iteration_time_window,
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"upload_compress_type": upload_compress_type,
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"upload_sparse_rate": upload_sparse_rate,
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"download_compress_type": download_compress_type,
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}
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ms.set_context(mode=ms.GRAPH_MODE, device_target=device_target)
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ms.set_fl_context(**ctx)
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if __name__ == "__main__":
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epoch = 5
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np.random.seed(0)
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network = LeNet5(62)
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criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
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net_adam_opt = AdamWeightDecayOp(network.trainable_params(), weight_decay=0.1)
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net_with_criterion = nn.WithLossCell(network, criterion)
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train_network = nn.TrainOneStepCell(net_with_criterion, net_opt)
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train_network.set_train()
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losses = []
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for _ in range(epoch):
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data = ms.Tensor(np.random.rand(32, 3, 32, 32).astype(np.float32))
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label = ms.Tensor(np.random.randint(0, 61, 32).astype(np.int32))
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loss = train_network(data, label).asnumpy()
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losses.append(loss)
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print(losses)
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