def poisoned_pixel_LDP(norm_bound, noise_scale, nb_attackers, 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_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)
# Poisonous updates
for idx in idxs_users[0:nb_attackers]:
local_model = LocalUpdate(args=args,
dataset=train_dataset,
idxs=user_groups[idx],
logger=logger)
w, _ = local_model.pixel_ldp(model=copy.deepcopy(global_model),
norm_bound=norm_bound,
noise_scale=noise_scale)
local_w.append(copy.deepcopy(w))
# Regular updates
for idx in idxs_users[nb_attackers:]:
local_model = LocalUpdate(args=args,
dataset=train_dataset,
idxs=user_groups[idx],
logger=logger)
w, _ = local_model.dp_sgd(model=copy.deepcopy(global_model),
norm_bound=norm_bound,
noise_scale=noise_scale)
local_w.append(copy.deepcopy(w))
# update global weights
global_weights = average_weights(local_w)
global_model.load_state_dict(global_weights)
# test accuracy
test_acc, test_loss, backdoor = test_backdoor_pixel(
args, global_model, test_dataset)
testing_accuracy.append(test_acc)
backdoor_accuracy.append(backdoor)
print("Testing & Backdoor accuracies")
print(testing_accuracy)
print(backdoor_accuracy)
# save test accuracy
np.savetxt(
'../save/PixelAttack/TestAcc/LDP_iid_{}_{}_clip{}_scale{}_attackers{}_seed{}.txt'
.format(args.dataset, args.model, norm_bound, noise_scale,
nb_attackers, s), testing_accuracy)
np.savetxt(
'../save/PixelAttack/BackdoorAcc/LDP_iid_{}_{}_clip{}_scale{}_attackers{}_seed{}.txt'
.format(args.dataset, args.model, norm_bound, noise_scale,
nb_attackers, s), backdoor_accuracy)
local_weights, local_losses = [], []
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)
for idx in idxs_users:
local_model = LocalUpdate(args=args,
dataset=train_dataset,
idxs=user_groups[idx],
logger=logger)
# Update local model idx
w, loss = local_model.dp_sgd(model=copy.deepcopy(global_model),
global_round=epoch,
norm_bound=args.norm_bound,
noise_scale=args.noise_scale)
local_weights.append(copy.deepcopy(w))
local_losses.append(copy.deepcopy(loss))
# average local model weights, update global model
global_weights = average_weights(local_weights)
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(testing_accuracy)
# save test accuracy
np.savetxt(