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loden_MIA.py
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loden_MIA.py
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from models import *
from constants import *
import pandas as pd
import numpy as np
import copy, os, random
import matplotlib.pyplot as plt
from common import DEVICE
torch.set_printoptions(precision=4, sci_mode=False)
class Organizer():
def __init__(self, train_epoch=TRAIN_EPOCH):
self.set_random_seed()
self.reader = DataReader()
self.target = TargetModel(self.reader)
self.bar_recorder = 0
self.last_acc = 0
self.attack_round = 0
def set_random_seed(self, seed=GLOBAL_SEED):
random.seed(seed)
np.random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
def honest_test(self, logger, adaptive=False, record_process=True, record_model=False, plot=False):
# Initialize data frame for recording purpose
acc_recorder = pd.DataFrame(columns=["epoch", "participant", "test_loss", "test_accuracy", "train_accuracy"])
attack_recorder = pd.DataFrame(columns=["epoch", \
"acc", "precision", "recall", "pred_acc_member", "pred_acc_non_member", \
"true_member", "false_member", "true_non_member", "false_non_member"])
param_recorder = pd.DataFrame()
attacker_success_round = []
# Initialize aggregator with given parameter size
aggregator = Aggregator(self.target.get_flatten_parameters(), DEFAULT_AGR)
logger.info("AGR is {}".format(DEFAULT_AGR))
logger.info("Dataset is {}".format(DEFAULT_SET))
logger.info("Member ratio is {}".format(BLACK_BOX_MEMBER_RATE))
member_total_samples = self.reader.get_honest_node_member()
member_total_samples = member_total_samples[2:100]
# Initialize attacker
logger.info(len(member_total_samples)//2)
for l in range(len(member_total_samples)//2):
# Initialize global model
self.attack_round = 0
global_model = FederatedModel(self.reader, aggregator)
global_model.init_global_model()
test_loss, test_acc = global_model.test_outcome()
# Recording and printing
if record_process:
acc_recorder.loc[len(acc_recorder)] = (0, "g", test_loss, test_acc, 0)
logger.info("Global model initiated, loss={}, acc={}".format(test_loss, test_acc))
# Initialize participants
participants = []
for i in range(NUMBER_OF_PARTICIPANTS):
participants.append(FederatedModel(self.reader, aggregator))
participants[i].init_participant(global_model, i)
test_loss, test_acc = participants[i].test_outcome()
try:
if DEFAULT_AGR == FANG or FL_TRUST:
aggregator.agr_model_acquire(global_model)
except NameError:
pass
logger.info("Participant {} initiated, loss={}, acc={}".format(i, test_loss, test_acc))
attacker_success_round = []
print(l)
attacker = WhiteBoxMalicious(self.reader, aggregator)
print(member_total_samples[0:2])
attacker.get_samples(member_total_samples[2*l:2*l+2])
logger.info(member_total_samples[2*l:2*l+2])
attacker_sample_list,member_list,nonmember_list = attacker.attacker_sample()
logger.info(member_list)
vector_hist = []
participant_0_hist = []
participant_0_hist_nonmember = []
grad_data = []
loss_set = []
participant_1_hist = []
participant_1_hist_nonmember = []
participant_2_hist = []
participant_2_hist_nonmember = []
participant_3_hist = []
participant_3_hist_nonmember = []
participant_4_hist = []
participant_4_hist_nonmember = []
monitor_recorder = {}
ascent_factor = ASCENT_FACTOR
for j in range(MAX_EPOCH):
# The global model's parameter is shared to each participant before every communication round
global_parameters = global_model.get_flatten_parameters()
train_acc_collector = []
for i in range(NUMBER_OF_PARTICIPANTS):
# The participants collect the global parameters before training
participants[i].collect_parameters(global_parameters)
if i == 0:
current_hist, attack_ground_0, label_status, out_list = participants[i].check_member_label(member_list)
current_nonmember_hist, nonmember_ground_0 = participants[i].check_nonmember_sample(i,
attacker_sample_list)
participant_0_hist.append(current_hist)
participant_0_hist_nonmember.append(current_nonmember_hist)
vector_hist.append(out_list)
print(out_list)
# predicted_vector_collector.loc[len(predicted_vector_collector)] = (out_list)
for index in range(len(out_list)):
logger.info("current epoch {}, attack sample {} prediction is {} groudtruth is {}".format(j,
out_list[
index][
3],
out_list[
index][
1],
out_list[
index][
2]))
# The participants calculate local gradients and share to the aggregator
participants[i].share_gradient()
train_loss, train_acc = participants[i].train_loss, participants[i].train_acc
train_acc_collector.append(train_acc)
# Printing and recording
test_loss, test_acc = participants[i].test_outcome()
try:
if DEFAULT_AGR == FANG or FL_TRUST:
aggregator.agr_model_acquire(global_model)
except NameError:
pass
logger.info(
"Epoch {} Participant {}, test_loss={}, test_acc={}, train_loss={}, train_acc={}".format(j + 1, i,
test_loss,
test_acc,
train_loss,
train_acc))
# attacker attack
attacker.collect_parameters(global_parameters)
true_member, false_member, true_non_member, false_non_member = attacker.evaluate_attack_result()
if true_member and false_member and true_non_member and false_non_member != 0:
attack_precision = true_member / (true_member + false_member)
attack_accuracy = (true_member + true_non_member) / (
true_member + true_non_member + false_member + false_non_member)
attack_recall = true_member / (true_member + false_non_member)
else:
attack_precision = (true_member + 1) / (true_member + false_member + 1)
attack_accuracy = (true_member + true_non_member + 1) / (
true_member + true_non_member + false_member + false_non_member + 1)
attack_recall = (true_member + 1) / (true_member + false_non_member + 1)
if j < TRAIN_EPOCH or self.attack_round > 40:
attacker.train()
else:
logger.info("attack!!")
logger.info("attack {}".format(self.attack_round))
try:
attacker.optimized_gradient_ascent(ascent_factor=ascent_factor,cover_factor=COVER_FACTOR)
except NameError:
attacker.optimized_gradient_ascent(ascent_factor=ascent_factor)
self.attack_round += 1
if DEFAULT_AGR in [FANG, MULTI_KRUM, KRUM]:
logger.info("Selected inputs are from participants number{}".format(aggregator.robust.appearence_list))
if 30 in aggregator.robust.appearence_list:
attacker_success_round.append(j)
logger.info("current status {}".format(str(aggregator.robust.status_list)))
logger.info("Attack accuracy = {}, Precision = {}, Recall={}".format(attack_accuracy, attack_precision,
attack_recall))
pred_acc_member = attacker.evaluate_member_accuracy()[0].cpu()
member_prediction = attacker.evaluate_member_accuracy()[1]
logger.info("member prediction {}".format(member_prediction))
pred_acc_non_member = attacker.evaluate_non_member_accuracy().cpu()
attack_recorder.loc[len(attack_recorder)] = (j + 1, \
attack_accuracy, attack_precision, attack_recall, \
pred_acc_member, pred_acc_non_member, \
true_member, false_member, true_non_member, false_non_member)
# Global model collects the aggregated gradient
global_model.apply_gradient()
loss_set.append(aggregator.robust.status_list+[j])
# Printing and recording
test_loss, test_acc = global_model.test_outcome()
train_acc = torch.mean(torch.tensor(train_acc_collector)).item()
self.last_acc = train_acc
if record_process:
acc_recorder.loc[len(acc_recorder)] = (j + 1, "g", test_loss, test_acc, train_acc)
logger.info(
"Epoch {} Global model, test_loss={}, test_acc={}, train_acc={}".format(j + 1, test_loss, test_acc,
train_acc))
self.print_log(0,participant_0_hist,participant_0_hist_nonmember,attack_ground_0, nonmember_ground_0)
logger.info("attack success round {}, total {}".format(attacker_success_round, len(attacker_success_round)))
predicted_vector_collector = pd.DataFrame(vector_hist)
predicted_vector_collector.to_csv(EXPERIMENTAL_DATA_DIRECTORY + TIME_STAMP + DEFAULT_SET + str(DEFAULT_AGR) + \
"TrainEpoch" + str(TRAIN_EPOCH) + "predicted_vector" + "_round" +str(l)+str(
MAX_EPOCH - TRAIN_EPOCH) + "optimized_model_single.csv")
if record_model:
param_recorder["global"] = global_model.get_flatten_parameters().detach().numpy()
for i in range(NUMBER_OF_PARTICIPANTS):
param_recorder["participant{}".format(i)] = participants[i].get_flatten_parameters().detach().numpy()
param_recorder.to_csv(EXPERIMENTAL_DATA_DIRECTORY + TIME_STAMP + DEFAULT_SET + "Federated_Models.csv")
if record_process:
recorder_suffix = "honest_node"
acc_recorder.to_csv(EXPERIMENTAL_DATA_DIRECTORY + TIME_STAMP + DEFAULT_SET + str(DEFAULT_AGR) + \
"TrainEpoch" + str(TRAIN_EPOCH) + "AttackEpoch" + str(
MAX_EPOCH - TRAIN_EPOCH) + recorder_suffix + "_round" +str(l)+"optimized_model_single_ascent_factor{}.csv".format(ASCENT_FACTOR))
attack_recorder.to_csv(EXPERIMENTAL_DATA_DIRECTORY + TIME_STAMP + DEFAULT_SET + str(DEFAULT_AGR) + \
"TrainEpoch" + str(TRAIN_EPOCH) + "AttackEpoch" + str(
MAX_EPOCH - TRAIN_EPOCH) + recorder_suffix + "_round" +str(l) +"optimized_attacker_single_ascent_factor{}.csv".format(ASCENT_FACTOR))
if plot:
self.plot_attack_performance(attack_recorder)
logger = make_logger("project", EXPERIMENTAL_DATA_DIRECTORY,
'log_{}_{}_{}_TrainEpoch{}_AttackEpoch{}_honest_member_rate{}_ascent_factor{}_single'.format(TIME_STAMP, DEFAULT_SET, DEFAULT_AGR,
TRAIN_EPOCH,
MAX_EPOCH - TRAIN_EPOCH,BLACK_BOX_MEMBER_RATE,ASCENT_FACTOR))
org = Organizer()
org.set_random_seed()
org.honest_test(logger)