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run_experiment_fig5_cifar.py
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run_experiment_fig5_cifar.py
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"""Run Experiment
This script allows to run one federated learning experiment; the experiment name, the method and the
number of clients/tasks should be precised along side with the hyper-parameters of the experiment.
The results of the experiment (i.e., training logs) are written to ./logs/ folder.
This file can also be imported as a module and contains the following function:
* run_experiment - runs one experiments given its arguments
"""
from utils.utils import *
from utils.constants import *
from utils.args import *
from run_experiment import *
from torch.utils.tensorboard import SummaryWriter
# Import General Libraries
import os
import argparse
import torch
import copy
import pickle
import random
import numpy as np
import pandas as pd
from models import *
# Import Transfer Attack
from transfer_attacks.Personalized_NN import *
from transfer_attacks.Params import *
from transfer_attacks.Transferer import *
from transfer_attacks.Args import *
from transfer_attacks.TA_utils import *
import numba
if __name__ == "__main__":
# exp_names = ['FedEMadv_K1',
# 'FedEMadv_K3',
# 'FedEMadv_K5',
# 'FedEMadv_K10',
# 'FedEMadv_K20',
# 'FedEMadv_K30']
# K_val = [1, 3, 5, 10, 20, 30]
exp_names = ['FedEMadv_K1',
'FedEMadv_K3',
'FedEMadv_K5',
'FedEMadv_K10']
K_val = [1, 3, 5, 10]
n_learners = 3
for itt in range(len(exp_names)):
print("running trial:", itt)
# Manually set argument parameters
args_ = Args()
args_.experiment = "cifar10"
args_.method = "FedEM_adv"
args_.decentralized = False
args_.sampling_rate = 1.0
args_.input_dimension = None
args_.output_dimension = None
args_.n_learners= n_learners
args_.n_rounds = 100
args_.bz = 128
args_.local_steps = 1
args_.lr_lambda = 0
args_.lr =0.03
args_.lr_scheduler = 'multi_step'
args_.log_freq = 10
args_.device = 'cuda'
args_.optimizer = 'sgd'
args_.mu = 0
args_.communication_probability = 0.1
args_.q = 1
args_.locally_tune_clients = False
args_.seed = 1234
args_.verbose = 1
args_.save_path = 'weights/final/cifar/fig5/' + exp_names[itt]
args_.validation = False
args_.save_freq = 10
# Other Argument Parameters
Q = 10 # update per round
G = 0.5
num_clients = 40
S = 0.05 # Threshold
step_size = 0.01
K = K_val[itt]
eps = 0.1
# Randomized Parameters
# Ru = np.random.uniform(low=R_val[itt]-0.1, high=R_val[itt]+0.1, size=num_clients)
Ru = np.ones(num_clients)
# Generate the dummy values here
aggregator, clients = dummy_aggregator(args_, num_clients)
# Set attack parameters
x_min = torch.min(clients[0].adv_nn.dataloader.x_data)
x_max = torch.max(clients[0].adv_nn.dataloader.x_data)
atk_params = PGD_Params()
atk_params.set_params(batch_size=1, iteration = K,
target = -1, x_val_min = x_min, x_val_max = x_max,
step_size = 0.05, step_norm = "inf", eps = eps, eps_norm = "inf")
# Obtain the central controller decision making variables (static)
num_h = args_.n_learners= 3
Du = np.zeros(len(clients))
for i in range(len(clients)):
num_data = clients[i].train_iterator.dataset.targets.shape[0]
Du[i] = num_data
D = np.sum(Du) # Total number of data points
# Train the model
print("Training..")
pbar = tqdm(total=args_.n_rounds)
current_round = 0
while current_round <= args_.n_rounds:
# If statement catching every Q rounds -- update dataset
if current_round != 0 and current_round%Q == 0: #
# print("Round:", current_round, "Calculation Adv")
# Obtaining hypothesis information
Whu = np.zeros([num_clients,num_h]) # Hypothesis weight for each user
for i in range(len(clients)):
# print("client", i)
temp_client = aggregator.clients[i]
hyp_weights = temp_client.learners_ensemble.learners_weights
Whu[i] = hyp_weights
row_sums = Whu.sum(axis=1)
Whu = Whu / row_sums[:, np.newaxis]
Wh = np.sum(Whu,axis=0)/num_clients
# Solve for adversarial ratio at every client
Fu = solve_proportions(G, num_clients, num_h, Du, Whu, S, Ru, step_size)
print(Fu)
# Assign proportion and attack params
# Assign proportion and compute new dataset
for i in range(len(clients)):
aggregator.clients[i].set_adv_params(Fu[i], atk_params)
aggregator.clients[i].update_advnn()
aggregator.clients[i].assign_advdataset()
aggregator.mix()
# Save more often the intermediate NN
# if current_round% args_.save_freq == 0:
# if "save_path" in args_:
# save_root = os.path.join(args_.save_path)
# os.makedirs(save_root, exist_ok=True)
# aggregator.save_state_intermed(save_root, current_round)
if aggregator.c_round != current_round:
pbar.update(1)
current_round = aggregator.c_round
if "save_path" in args_:
save_root = os.path.join(args_.save_path)
os.makedirs(save_root, exist_ok=True)
aggregator.save_state(save_root)
del args_, aggregator, clients
torch.cuda.empty_cache()