if np.abs(acc_1 - acc_2) < 0.03:
flag = True
return ter_dict, flag
else:
return f_dict, flag
if __name__ == '__main__':
torch.manual_seed(Args.seed)
C_iter, train_iter, test_iter, stats = data_utils.get_dataset(args=Args)
# build global network
G_net = Fed_Model()
print(G_net)
G_net.train()
G_loss_fun = torch.nn.CrossEntropyLoss()
# copy weights
w_glob = G_net.state_dict()
m = max(int(Args.frac * Args.num_C), 1)
gv_acc = []
net_best = None
val_acc_list, net_list = [], []
num_s2 = 0
# training
c_lists = [[] for i in range(Args.num_C)]
for rounds in range(Args.rounds):
