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- import os
- import logging
- import copy
- import h5py
- from flcore.clients.clientpfedme import clientpFedMe
- from flcore.servers.serverbase import Server
- class pFedMe(Server):
- def __init__(self, args, times):
- super().__init__(args, times)
- self.message_hp = f"{args.algorithm}, lr:{args.local_learning_rate:.5f}"
- clientObj = clientpFedMe
- self.message_hp_dash = self.message_hp.replace(", ", "-")
- self.hist_result_fn = os.path.join(args.hist_dir, f"{self.actual_dataset}-{self.message_hp_dash}-{args.goal}-{self.times}.h5")
- self.set_clients(args, clientObj)
- self.beta = args.beta
- self.rs_train_acc_per = []
- self.rs_train_loss_per = []
- self.rs_test_acc_per = []
- print(f"\nJoin ratio / total clients: {self.join_ratio} / {self.num_clients}")
- print("Finished creating server and clients.")
- def train(self):
- for i in range(self.global_rounds):
- self.selected_clients = self.select_clients()
- self.send_models()
-
- print(f"\n------------- Round number: [{i+1:3d}/{self.global_rounds}]-------------")
- print(f"==> Training for {len(self.selected_clients)} clients...", flush=True)
- for client in self.selected_clients:
- client.train()
- if i%self.eval_gap == 0:
- print("==> Evaluating personalized models...")
- self.evaluate()
- self.previous_global_model = copy.deepcopy(list(self.global_model.parameters()))
- self.receive_models()
- self.aggregate_parameters()
- self.beta_aggregate_parameters()
- self.save_results(fn=self.hist_result_fn)
- message_res = f"\ttest_acc:{self.best_mean_test_acc:.6f}"
- logging.info(self.message_hp + message_res)
- # self.save_global_model()
- def beta_aggregate_parameters(self):
- # aggregate avergage model with previous model using parameter beta
- for pre_param, param in zip(self.previous_global_model, self.global_model.parameters()):
- param.data = (1 - self.beta)*pre_param.data + self.beta*param.data
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