def setup_clients(self, train_data_local_num_dict, train_data_local_dict, test_data_local_dict):
logging.info("############setup_clients (START)#############")
for client_idx in range(self.args.client_num_per_round):
c = Client(client_idx, train_data_local_dict[client_idx], test_data_local_dict[client_idx],
train_data_local_num_dict[client_idx], self.args, self.device)
self.client_list.append(c)
logging.info("############setup_clients (END)#############")
def _setup_clients(self, train_data_local_num_dict, train_data_local_dict,
test_data_local_dict, model_trainer):
logging.info("############setup_clients (START)#############")
# 补充：判断总客户端数量是否小于每轮参与的客户端数量
if self.args.client_num_in_total < self.args.client_num_per_round:
logging.info(
"client_num_in_total is less than client_num_per_round, please check params"
)
# 根据每轮参与的客户端数量生成相应个数的客户端(每轮参与的基本客户端列表)
for client_idx in range(self.args.client_num_per_round):
c = Client(client_idx, train_data_local_dict[client_idx],
test_data_local_dict[client_idx],
train_data_local_num_dict[client_idx], self.args,
self.device, model_trainer)
self.client_list.append(c)
logging.info("############setup_clients (END)#############")
def setup_clients(self):
self.logger.info("############setup_clients (START)#############")
args_datadir = "./data/cifar10"
for client_idx in range(self.args.client_number):
self.logger.info("######client idx = " + str(client_idx))
dataidxs = self.net_dataidx_map[client_idx]
local_sample_number = len(dataidxs)
# training batch size = 64; test batch size = 32
train_dl_local, test_dl_local = get_dataloader(
self.args.dataset, args_datadir, self.args.batch_size, 32,
dataidxs)
self.logger.info('n_sample: %d' % local_sample_number)
self.logger.info('n_training: %d' % len(train_dl_local))
self.logger.info('n_test: %d' % len(test_dl_local))
c = Client(train_dl_local, test_dl_local, local_sample_number,
self.args, self.logger, self.device)
self.client_list.append(c)
self.logger.info("############setup_clients (END)#############")