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case3.py
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case3.py
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import copy
import os
import pickle
import time
from copy import deepcopy
import numpy as np
import torch
from torch.nn.utils import parameters_to_vector, vector_to_parameters
from tqdm import tqdm
import config
from unlearn.pga import get_model_ref, get_ref_vec, get_threshold, unlearn
from utils import clients, server
from utils.dataloader import get_loaders
from utils.model import get_model
from utils.utils import get_results, save_param, update_results, load_results
np.random.seed(42)
torch.manual_seed(42)
torch.cuda.manual_seed(42)
torch.backends.cudnn.enabled = False
torch.backends.cudnn.deterministic = True
"""
PGA
https://arxiv.org/pdf/2207.05521.pdf
"""
if __name__ == "__main__":
args = config.get_args()
train_loaders, test_loader, test_loader_poison = get_loaders(args)
num_rounds = args.num_rounds
num_unlearn_rounds = args.num_unlearn_rounds
num_post_training_rounds = args.num_post_training_rounds
num_onboarding_rounds = args.num_onboarding_rounds
if not args.is_onboarding:
start_time = time.time()
res = get_results(args)
model_ref = get_model_ref(args)
# train and evaluate the FL model
model = copy.deepcopy(model_ref)
param_ref = model_ref.state_dict()
party0_param = torch.load(
f"./results/models/case0/client0/case0_{args.dataset}_C{args.num_clients}_BS{args.batch_size}_R{args.num_rounds}_UR{args.num_unlearn_rounds}_PR{args.num_post_training_rounds}_E{args.local_epochs}_LR{args.lr}_round{args.num_rounds - 1}.pt"
)
global_param = copy.deepcopy(param_ref)
party_to_be_erased = 0
threshold = get_threshold(args, model_ref)
end_round = num_rounds + num_unlearn_rounds
print(f"------------Unlearn------------")
for round in range(num_rounds, end_round):
print(
"Round {}/{}: lr {} {}".format(round + 1, end_round, args.lr, args.out_file)
)
train_loss, test_loss = 0, 0
train_corr, test_acc = 0, 0
train_total = 0
list_params = []
tem_param, unlearn_summ = unlearn(
args=args,
param=global_param,
param_ref=param_ref,
party0_param=party0_param,
distance_threshold=2.2,
loader=train_loaders[party_to_be_erased],
threshold=threshold,
clip_grad=5,
epochs=1,
)
global_param = tem_param
# save global param
folder_path = f"./results/models/case3"
os.makedirs(folder_path, exist_ok=True)
torch.save(
global_param,
f'{folder_path}/{args.out_file.split("/")[-1].split(".pkl")[0]}_round{round}.pt',
)
res = update_results(args, res, global_param, test_loader, test_loader_poison)
print(f'Global clean accuracy: {res["val"]["acc"]["clean"][-1]}')
print(f'Global backdoor accuracy: {res["val"]["acc"]["backdoor"][-1]}')
total_time = time.time() - start_time
res["time"] = total_time
print(f"Time {total_time}")
######################## post train ############################
start_round = end_round
end_round = start_round + args.num_post_training_rounds
for round in range(start_round, end_round):
print(
"Round {}/{}: lr {} {}".format(round + 1, end_round, args.lr, args.out_file)
)
train_loss, test_loss = 0, 0
train_corr, test_acc = 0, 0
train_total = 0
list_params = []
chosen_clients = [i for i in range(1, args.num_clients)]
for client in tqdm(chosen_clients):
print(f"-----------client {client} starts training----------")
tem_param, train_summ = clients.client_train(
args,
deepcopy(global_param),
train_loaders[client],
epochs=args.local_epochs,
)
# save client params
# save_param(
# args,
# param=tem_param,
# case=3,
# client=client,
# round=round,
# is_global=False,
# )
train_loss += train_summ["loss"]
train_corr += train_summ["correct"]
train_total += train_summ["total"]
list_params.append(tem_param)
res["train"]["loss"]["avg"].append(train_loss / len(chosen_clients))
res["train"]["acc"]["avg"].append(train_corr / train_total)
print(
"Train loss: {:5f} acc: {:5f}".format(
res["train"]["loss"]["avg"][-1],
res["train"]["acc"]["avg"][-1],
)
)
# server aggregation
global_param = server.FedAvg(list_params)
# save global param
save_param(args, param=global_param, case=3, round=round)
res = update_results(args, res, global_param, test_loader, test_loader_poison)
with open(args.out_file, "wb") as fp:
pickle.dump(res, fp)
else:
######################## onboarding round ############################
model = get_model(args)
global_param = model.state_dict()
start_round = num_rounds + num_unlearn_rounds + num_post_training_rounds
end_round = start_round + num_onboarding_rounds
global_param = torch.load(
f"./results/models/case3/case3_{args.dataset}_C{args.num_clients}_BS{args.batch_size}_R{args.num_rounds}_UR{args.num_unlearn_rounds}_PR{args.num_post_training_rounds}_E{args.local_epochs}_LR{args.lr}_round{start_round-1}.pt"
)
res = load_results(
f"./results/case3_{args.dataset}_C{args.num_clients}_BS{args.batch_size}_R{args.num_rounds}_UR{args.num_unlearn_rounds}_PR{args.num_post_training_rounds}_E{args.local_epochs}_LR{args.lr}.pkl"
)
for round in range(start_round, end_round):
print(
"Round {}/{}: lr {} {}".format(round + 1, end_round, args.lr, args.out_file)
)
train_loss, test_loss = 0, 0
train_corr, test_acc = 0, 0
train_total = 0
list_params = []
chosen_clients = [i for i in range(args.num_clients)]
for client in tqdm(chosen_clients):
print(f"-----------client {client} starts training----------")
tem_param, train_summ = clients.client_train(
args,
deepcopy(global_param),
train_loaders[client],
epochs=args.local_epochs,
)
# save client params
# save_param(
# args,
# param=tem_param,
# case=3,
# client=client,
# round=round,
# is_global=False,
# )
train_loss += train_summ["loss"]
train_corr += train_summ["correct"]
train_total += train_summ["total"]
list_params.append(tem_param)
res["train"]["loss"]["avg"].append(train_loss / len(chosen_clients))
res["train"]["acc"]["avg"].append(train_corr / train_total)
print(
"Train loss: {:5f} acc: {:5f}".format(
res["train"]["loss"]["avg"][-1],
res["train"]["acc"]["avg"][-1],
)
)
# server aggregation
global_param = server.FedAvg(list_params)
# save global param
save_param(args, param=global_param, case=3, round=round)
res = update_results(args, res, global_param, test_loader, test_loader_poison)
with open(args.out_file, "wb") as fp:
pickle.dump(res, fp)
# total_time = time.time() - start_time
# res["time"] = total_time
# print(f"Time {total_time}")
# with open(args.out_file, "wb") as fp:
# pickle.dump(res, fp)