def poisoned_1to7_NoDefense(seed=1):
start_time = time.time()
# define paths
path_project = os.path.abspath('..')
logger = SummaryWriter('../logs')
args = args_parser()
exp_details(args)
# set seed
torch.manual_seed(seed)
np.random.seed(seed)
# device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# load dataset and user groups
train_dataset, test_dataset, user_groups = get_dataset(args)
# BUILD MODEL
if args.model == 'cnn':
# Convolutional neural netork
if args.dataset == 'mnist':
global_model = CNNMnist(args=args)
elif args.dataset == 'fmnist':
global_model = CNNFashion_Mnist(args=args)
elif args.dataset == 'cifar':
global_model = CNNCifar(args=args)
elif args.model == 'mlp':
# Multi-layer preceptron
img_size = train_dataset[0][0].shape
len_in = 1
for x in img_size:
len_in *= x
global_model = MLP(dim_in=len_in,
dim_hidden=64,
dim_out=args.num_classes)
else:
exit('Error: unrecognized model')
# Set the model to train and send it to device.
global_model.to(device)
global_model.train()
print(global_model)
# copy weights
global_weights = global_model.state_dict()
# testing accuracy for global model
testing_accuracy = [0.1]
backdoor_accuracy = [0.1]
for epoch in tqdm(range(args.epochs)):
local_del_w, local_norms = [], []
print(f'\n | Global Training Round : {epoch+1} |\n')
global_model.train()
m = max(int(args.frac * args.num_users), 1)
idxs_users = np.random.choice(range(args.num_users), m, replace=False)
# Adversary updates
print("Evil norms:")
for idx in idxs_users[0:args.nb_attackers]:
local_model = LocalUpdate(args=args,
dataset=train_dataset,
idxs=user_groups[idx],
logger=logger)
del_w, zeta = local_model.poisoned_1to7(
model=copy.deepcopy(global_model), change=1)
local_del_w.append(copy.deepcopy(del_w))
local_norms.append(copy.deepcopy(zeta))
print(zeta)
# Non-adversarial updates
print("Good norms:")
for idx in idxs_users[args.nb_attackers:]:
print(idx)
local_model = LocalUpdate(args=args,
dataset=train_dataset,
idxs=user_groups[idx],
logger=logger)
del_w, zeta = local_model.update_weights(
model=copy.deepcopy(global_model), change=1)
local_del_w.append(copy.deepcopy(del_w))
local_norms.append(copy.deepcopy(zeta))
print(zeta)
# average local updates
average_del_w = average_weights(local_del_w)
# Update global model: w_{t+1} = w_{t} + average_del_w
for param, param_del_w in zip(global_weights.values(),
average_del_w.values()):
param += param_del_w
global_model.load_state_dict(global_weights)
# test accuracy, backdoor accuracy
test_acc, test_loss, back_acc = test_inference1to7(
args, global_model, test_dataset)
testing_accuracy.append(test_acc)
backdoor_accuracy.append(back_acc)
print("Test & Backdoor accuracy")
print(testing_accuracy)
print(backdoor_accuracy)
# save accuracy
np.savetxt(
'../save/1to7Attack/TestAcc/NoDefense_{}_{}_seed{}.txt'.format(
args.dataset, args.model, s), testing_accuracy)
np.savetxt(
'../save/1to7Attack/BackAcc/NoDefense_{}_{}_seed{}.txt'.format(
args.dataset, args.model, s), backdoor_accuracy)
def poisoned_random_CDP(seed=1):
# Central DP to protect against attackers
start_time = time.time()
# define paths
path_project = os.path.abspath('..')
logger = SummaryWriter('../logs')
args = args_parser()
exp_details(args)
# set seed
torch.manual_seed(seed)
np.random.seed(seed)
# device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# load dataset and user groups
train_dataset, test_dataset, user_groups = get_dataset(args)
# BUILD MODEL
if args.model == 'cnn':
# Convolutional neural netork
if args.dataset == 'mnist':
global_model = CNNMnist(args=args)
elif args.dataset == 'fmnist':
global_model = CNNFashion_Mnist(args=args)
elif args.dataset == 'cifar':
global_model = CNNCifar(args=args)
elif args.model == 'mlp':
# Multi-layer preceptron
img_size = train_dataset[0][0].shape
len_in = 1
for x in img_size:
len_in *= x
global_model = MLP(dim_in=len_in,
dim_hidden=64,
dim_out=args.num_classes)
else:
exit('Error: unrecognized model')
# Set the model to train and send it to device.
global_model.to(device)
global_model.train()
print(global_model)
# copy weights
global_weights = global_model.state_dict()
# testing accuracy for global model
testing_accuracy = [0.1]
backdoor_accuracy = [0.1]
for epoch in tqdm(range(args.epochs)):
local_del_w, local_norms = [], []
print(f'\n | Global Training Round : {epoch+1} |\n')
global_model.train()
m = max(int(args.frac * args.num_users), 1)
idxs_users = np.random.choice(range(args.num_users), m, replace=False)
# Adversaries' update
for idx in idxs_users[0:args.nb_attackers]:
local_model = LocalUpdate(args=args,
dataset=train_dataset,
idxs=user_groups[idx],
logger=logger)
del_w, zeta = local_model.poisoned_1to7(
model=copy.deepcopy(global_model), change=1)
local_del_w.append(copy.deepcopy(del_w))
local_norms.append(copy.deepcopy(zeta))
# Non-adversary updates
for idx in idxs_users[args.nb_attackers:]:
local_model = LocalUpdate(args=args,
dataset=train_dataset,
idxs=user_groups[idx],
logger=logger)
del_w, zeta = local_model.update_weights(
model=copy.deepcopy(global_model), change=1)
local_del_w.append(copy.deepcopy(del_w))
local_norms.append(copy.deepcopy(zeta))
# norm bound (e.g. median of norms)
median_norms = args.norm_bound #np.median(local_norms) #args.norm_bound
print(median_norms)
# clip weight updates
for i in range(len(idxs_users)):
for param in local_del_w[i].values():
param /= max(1, local_norms[i] / median_norms)
# average the clipped weight updates
average_del_w = average_weights(local_del_w)
# Update global model using clipped weight updates, and add noise
# w_{t+1} = w_{t} + avg(del_w1 + del_w2 + ... + del_wc) + Noise
for param, param_del_w in zip(global_weights.values(),
average_del_w.values()):
param += param_del_w
param += torch.randn(
param.size()) * args.noise_scale * median_norms / (
len(idxs_users)**0.5)
global_model.load_state_dict(global_weights)
# test accuracy
test_acc, test_loss = test_inference(args, global_model, test_dataset)
testing_accuracy.append(test_acc)
print("Test accuracy")
print(testing_accuracy)
# save test accuracy
np.savetxt(
'../save/1to7Attack/GDP_{}_{}_seed{}_clip{}_scale{}.txt'.format(
args.dataset, args.model, s, args.norm_bound, args.noise_scale),
testing_accuracy)
