class FL_client():
def __init__(self, args):
if args.dataset == 'cifar':
self.net = CNNCifar(args=args).to(args.device)
else:
self.net = CNNMnist(args=args).to(args.device)
self.net.train()
self.loss_func = nn.CrossEntropyLoss()
self.optimizer = torch.optim.SGD(self.net.parameters(), lr=args.lr)
self.args = args
self.w_glob = []
# key exchange
self.x = self.gx = 0
self.keys = defaultdict(int)
def set_data(self, dataset, idxs):
self.data = DataLoader(DatasetSplit(dataset, idxs),
batch_size=self.args.local_bs,
shuffle=True)
def load_state(self, state_dict):
self.net.load_state_dict(state_dict)
def train(self):
epoch_loss = []
for _ in range(self.args.local_ep):
batch_loss = []
for _, (images, labels) in enumerate(self.data):
images, labels = images.to(self.args.device), labels.to(
self.args.device)
pred = self.net(images)
loss = self.loss_func(pred, labels)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
batch_loss.append(loss.item())
epoch_loss.append(sum(batch_loss) / len(batch_loss))
return self.net.state_dict(), sum(epoch_loss) / len(epoch_loss)
net_glob15 = customCNNCifar(args=args).to(args.device)
net_glob20 = customCNNCifar(args=args).to(args.device)
net_glob25 = customCNNCifar(args=args).to(args.device)
net_glob30 = customCNNCifar(args=args).to(args.device)
elif args.model == 'cnn' and args.dataset == 'mnist':
net_glob = CNNMnist(args=args).to(args.device)
elif args.model == 'mlp':
len_in = 1
for x in img_size:
len_in *= x
net_glob = MLP(dim_in=len_in, dim_hidden=64, dim_out=args.num_classes).to(args.device)
else:
exit('Error: unrecognized model')
print(net_glob)
net_glob.train()
net_glob1.train()
net_glob5.train()
net_glob10.train()
net_glob15.train()
net_glob20.train()
net_glob25.train()
net_glob30.train()
# copy weights
w_glob = net_glob.state_dict()
w_glob1 = net_glob1.state_dict()
w_glob5 = net_glob5.state_dict()
w_glob10 = net_glob10.state_dict()
w_glob15 = net_glob15.state_dict()
w_glob20 = net_glob20.state_dict()
net_glob10 = CNNMnist(args=args).to(args.device)
net_glob15 = CNNMnist(args=args).to(args.device)
net_glob20 = CNNMnist(args=args).to(args.device)
net_glob25 = CNNMnist(args=args).to(args.device)
net_glob30 = CNNMnist(args=args).to(args.device)
elif args.model == 'mlp':
len_in = 1
for x in img_size:
len_in *= x
net_glob = MLP(dim_in=len_in, dim_hidden=64,
dim_out=args.num_classes).to(args.device)
else:
exit('Error: unrecognized model')
print(net_glob)
net_glob.train()
net_glob1.train()
net_glob5.train()
net_glob10.train()
net_glob15.train()
net_glob20.train()
net_glob25.train()
net_glob30.train()
# copy weights
w_glob = net_glob.state_dict()
w_glob1 = net_glob1.state_dict()
w_glob5 = net_glob5.state_dict()
w_glob10 = net_glob10.state_dict()
w_glob15 = net_glob15.state_dict()
w_glob20 = net_glob20.state_dict()
w_glob25 = net_glob25.state_dict()
elif args.model == 'cnn' and args.dataset == 'mnist':
net_glob = CNNMnist(args=args).to(args.device)
net_glob1 = CNNMnist(args=args).to(args.device)
net_glob5 = CNNMnist(args=args).to(args.device)
net_glob7 = CNNMnist(args=args).to(args.device)
net_glob10 = CNNMnist(args=args).to(args.device)
elif args.model == 'mlp':
len_in = 1
for x in img_size:
len_in *= x
net_glob = MLP(dim_in=len_in, dim_hidden=64, dim_out=args.num_classes).to(args.device)
else:
exit('Error: unrecognized model')
print(net_glob)
net_glob.train()
net_glob1.train()
net_glob5.train()
net_glob7.train()
net_glob10.train()
# copy weights
w_glob = net_glob.state_dict()
w_glob1 = net_glob1.state_dict()
w_glob5 = net_glob5.state_dict()
w_glob7 = net_glob7.state_dict()
w_glob10 = net_glob10.state_dict()
# training - NO ATTACK
loss_train = []
cv_loss, cv_acc = [], []
val_loss_pre, counter = 0, 0
net_best = None
# if param.grad is not None:
# print(type(param))
# param_norm = param.grad.data.norm(p)
# total_norm += param_norm.item() ** p
# total_norm = total_norm ** (1. / p)
# return total_norm
def norm1(x, p):
"First-pass implementation of p-norm."
return (abs(x)**p).sum()**(1. / p)
#print(norm1(net_glob.state_dict(),1))
#net_glob.load_state_dict()
m = net_glob.train()
net_glob.eval()
#print(type(net_glob.state_dict()))
#print(net_glob.state_dict().keys())
#data = list(net_glob.state_dict().items())
#an_array = np.array(data)
#for layer in net_glob.ordered_layers:
# norm_grad = layer.weight.grad.norm()
# tone = f + ((norm_grad.numpy()) * 100.0)
best_state = copy.deepcopy(net_glob.state_dict())
#print(net_glob.grad)
print(pnorm(net_glob, 2))
#print(torch.norm(net_glob, dim=None, p=2))
#print(net_glob.state_dict().grad.norm(1))