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- from flcore.clients.clientper import clientPer
- from flcore.servers.serverbase import Server
- import copy
- import os
- import logging
- class FedPer(Server):
- def __init__(self, args, times):
- super().__init__(args, times)
- self.message_hp = f"{args.algorithm}, lr:{args.local_learning_rate:.5f}"
- clientObj = clientPer
- 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)
- print(f"\nJoin ratio / total clients: {self.join_ratio} / {self.num_clients}")
- print("Finished creating server and clients.")
- # self.load_model()
- 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()
- self.receive_models()
- self.aggregate_parameters()
-
- if i%self.eval_gap == 0:
- print("==> Evaluating personalized models...", flush=True)
- self.send_models(mode="all")
- self.evaluate(self.global_model)
- print(f"==> Best mean personalized accuracy: {self.best_mean_test_acc*100:.2f}%", flush=True)
- 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)
- def receive_models(self):
- assert (len(self.selected_clients) > 0)
- self.uploaded_weights = []
- tot_samples = 0
- self.uploaded_ids = []
- self.uploaded_models = []
- for client in self.selected_clients:
- self.uploaded_weights.append(client.train_samples)
- tot_samples += client.train_samples
- self.uploaded_ids.append(client.id)
- self.uploaded_models.append(client.model.base)
- for i, w in enumerate(self.uploaded_weights):
- self.uploaded_weights[i] = w / tot_samples
- def prepare_global_model(self):
- temp_model = copy.deepcopy(self.global_model) # base
- self.global_model = copy.deepcopy(self.clients[0].model)
- for p_t, p_g in zip(temp_model.parameters(), self.global_model.base.parameters()):
- p_g.data = p_t.data.clone()
- for p in self.global_model.predictor.parameters():
- p.data.zero_()
- for c in self.clients:
- for p_g, p_c in zip(self.global_model.predictor.parameters(), c.model.predictor.parameters()):
- p_g.data += p_c.data * c.train_samples
- return
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