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Experiments_MNIST_EXTREME_NON_IID.py
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#!/usr/bin/env python
# coding: utf-8
# In[ ]:
#================================= Start of importing required packages and libraries =========================================#
from __future__ import print_function
get_ipython().run_line_magic('matplotlib', 'inline')
import numpy as np
import torch
from experiment_federated import *
import random
#================================== End of importing required packages and libraries ==========================================#
# In[ ]:
#=============================== Defining global variables ========================#
DATASET_NAME = "MNIST"
MODEL_NAME = "CNNMNIST"
DD_TYPE = 'EXTREME_NON_IID'
ALPHA = 1
NUM_PEERS = 100 # "number of peers: K"
FRAC_PEERS = 1 #'the fraction of peers: C to bel selected in each round'
SEED = 7 #fixed seed
random.seed(SEED)
CRITERION = nn.CrossEntropyLoss()
GLOBAL_ROUNDS = 200 #"number of rounds of federated model training"
LOCAL_EPOCHS = 3# "the number of local epochs: E for each peer"
TEST_BATCH_SIZE = 1000
LOCAL_BS = 64 #"local batch size: B for each peer"
LOCAL_LR = 0.001#local learning rate: lr for each peer
LOCAL_MOMENTUM = 0.9 #local momentum for each peer
NUM_CLASSES = 10 # number of classes in an experiment
LABELS_DICT = {'Zero':0,
'One':1,
'Two':2,
'Three':3,
'Four':4,
'Five':5,
'Six':6,
'Seven':7,
'Eight':8,
'Nine':9}
#select the device to work with cpu or gpu
if torch.cuda.is_available():
DEVICE = "cuda"
else:
DEVICE = "cpu"
DEVICE = torch.device(DEVICE)
SOURCE_CLASS = 7 # the source class
TARGET_CLASS = 1 # the target class
CLASS_PER_PEER = 1
SAMPLES_PER_CLASS = 582
RATE_UNBALANCE = 1
# In[ ]:
# Baseline|: FedAvg-no attacks (FL)
RULE = 'fedavg'
ATTACK_TYPE='label_flipping'
MALICIOUS_BEHAVIOR_RATE = 1
for atr in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]:
run_exp(dataset_name = DATASET_NAME, model_name = MODEL_NAME, dd_type = DD_TYPE, num_peers = NUM_PEERS,
frac_peers = FRAC_PEERS, seed = SEED, test_batch_size = TEST_BATCH_SIZE,
criterion = CRITERION, global_rounds = GLOBAL_ROUNDS, local_epochs = LOCAL_EPOCHS, local_bs = LOCAL_BS,
local_lr = LOCAL_LR, local_momentum = LOCAL_MOMENTUM, labels_dict = LABELS_DICT, device = DEVICE,
attackers_ratio = atr, attack_type=ATTACK_TYPE,
malicious_behavior_rate = MALICIOUS_BEHAVIOR_RATE, rule = RULE,
source_class = SOURCE_CLASS, target_class = TARGET_CLASS,
class_per_peer = CLASS_PER_PEER, samples_per_class = SAMPLES_PER_CLASS,
rate_unbalance = RATE_UNBALANCE, alpha = ALPHA, resume = False)
# In[ ]:
RULE = 'median'
ATTACK_TYPE='label_flipping'
MALICIOUS_BEHAVIOR_RATE = 1
for atr in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]:
run_exp(dataset_name = DATASET_NAME, model_name = MODEL_NAME, dd_type = DD_TYPE, num_peers = NUM_PEERS,
frac_peers = FRAC_PEERS, seed = SEED, test_batch_size = TEST_BATCH_SIZE,
criterion = CRITERION, global_rounds = GLOBAL_ROUNDS, local_epochs = LOCAL_EPOCHS, local_bs = LOCAL_BS,
local_lr = LOCAL_LR, local_momentum = LOCAL_MOMENTUM, labels_dict = LABELS_DICT, device = DEVICE,
attackers_ratio = atr, attack_type=ATTACK_TYPE,
malicious_behavior_rate = MALICIOUS_BEHAVIOR_RATE, rule = RULE,
source_class = SOURCE_CLASS, target_class = TARGET_CLASS,
class_per_peer = CLASS_PER_PEER, samples_per_class = SAMPLES_PER_CLASS,
rate_unbalance = RATE_UNBALANCE, alpha = ALPHA, resume = False)
# In[ ]:
RULE = 'rmedian'
ATTACK_TYPE='label_flipping'
MALICIOUS_BEHAVIOR_RATE = 1
for atr in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]:
run_exp(dataset_name = DATASET_NAME, model_name = MODEL_NAME, dd_type = DD_TYPE, num_peers = NUM_PEERS,
frac_peers = FRAC_PEERS, seed = SEED, test_batch_size = TEST_BATCH_SIZE,
criterion = CRITERION, global_rounds = GLOBAL_ROUNDS, local_epochs = LOCAL_EPOCHS, local_bs = LOCAL_BS,
local_lr = LOCAL_LR, local_momentum = LOCAL_MOMENTUM, labels_dict = LABELS_DICT, device = DEVICE,
attackers_ratio = atr, attack_type=ATTACK_TYPE,
malicious_behavior_rate = MALICIOUS_BEHAVIOR_RATE, rule = RULE,
source_class = SOURCE_CLASS, target_class = TARGET_CLASS,
class_per_peer = CLASS_PER_PEER, samples_per_class = SAMPLES_PER_CLASS,
rate_unbalance = RATE_UNBALANCE, alpha = ALPHA, resume = False)
# In[ ]:
RULE = 'tmean'
ATTACK_TYPE='label_flipping'
MALICIOUS_BEHAVIOR_RATE = 1
for atr in [0.0, 0.1, 0.2, 0.3, 0.4]:
run_exp(dataset_name = DATASET_NAME, model_name = MODEL_NAME, dd_type = DD_TYPE, num_peers = NUM_PEERS,
frac_peers = FRAC_PEERS, seed = SEED, test_batch_size = TEST_BATCH_SIZE,
criterion = CRITERION, global_rounds = GLOBAL_ROUNDS, local_epochs = LOCAL_EPOCHS, local_bs = LOCAL_BS,
local_lr = LOCAL_LR, local_momentum = LOCAL_MOMENTUM, labels_dict = LABELS_DICT, device = DEVICE,
attackers_ratio = atr, attack_type=ATTACK_TYPE,
malicious_behavior_rate = MALICIOUS_BEHAVIOR_RATE, rule = RULE,
source_class = SOURCE_CLASS, target_class = TARGET_CLASS,
class_per_peer = CLASS_PER_PEER, samples_per_class = SAMPLES_PER_CLASS,
rate_unbalance = RATE_UNBALANCE, alpha = ALPHA, resume = False)
# In[ ]:
RULE = 'mkrum'
ATTACK_TYPE='label_flipping'
MALICIOUS_BEHAVIOR_RATE = 1
for atr in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]:
run_exp(dataset_name = DATASET_NAME, model_name = MODEL_NAME, dd_type = DD_TYPE, num_peers = NUM_PEERS,
frac_peers = FRAC_PEERS, seed = SEED, test_batch_size = TEST_BATCH_SIZE,
criterion = CRITERION, global_rounds = GLOBAL_ROUNDS, local_epochs = LOCAL_EPOCHS, local_bs = LOCAL_BS,
local_lr = LOCAL_LR, local_momentum = LOCAL_MOMENTUM, labels_dict = LABELS_DICT, device = DEVICE,
attackers_ratio = atr, attack_type=ATTACK_TYPE,
malicious_behavior_rate = MALICIOUS_BEHAVIOR_RATE, rule = RULE,
source_class = SOURCE_CLASS, target_class = TARGET_CLASS,
class_per_peer = CLASS_PER_PEER, samples_per_class = SAMPLES_PER_CLASS,
rate_unbalance = RATE_UNBALANCE, alpha = ALPHA, resume = False)
# In[ ]:
RULE = 'foolsgold'
ATTACK_TYPE='label_flipping'
MALICIOUS_BEHAVIOR_RATE = 1
for atr in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]:
run_exp(dataset_name = DATASET_NAME, model_name = MODEL_NAME, dd_type = DD_TYPE, num_peers = NUM_PEERS,
frac_peers = FRAC_PEERS, seed = SEED, test_batch_size = TEST_BATCH_SIZE,
criterion = CRITERION, global_rounds = GLOBAL_ROUNDS, local_epochs = LOCAL_EPOCHS, local_bs = LOCAL_BS,
local_lr = LOCAL_LR, local_momentum = LOCAL_MOMENTUM, labels_dict = LABELS_DICT, device = DEVICE,
attackers_ratio = atr, attack_type=ATTACK_TYPE,
malicious_behavior_rate = MALICIOUS_BEHAVIOR_RATE, rule = RULE,
source_class = SOURCE_CLASS, target_class = TARGET_CLASS,
class_per_peer = CLASS_PER_PEER, samples_per_class = SAMPLES_PER_CLASS,
rate_unbalance = RATE_UNBALANCE, alpha = ALPHA, resume = False)
# In[ ]:
RULE = 'lfa_defender'
ATTACK_TYPE='label_flipping'
MALICIOUS_BEHAVIOR_RATE = 1
for atr in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]:
run_exp(dataset_name = DATASET_NAME, model_name = MODEL_NAME, dd_type = DD_TYPE, num_peers = NUM_PEERS,
frac_peers = FRAC_PEERS, seed = SEED, test_batch_size = TEST_BATCH_SIZE,
criterion = CRITERION, global_rounds = GLOBAL_ROUNDS, local_epochs = LOCAL_EPOCHS, local_bs = LOCAL_BS,
local_lr = LOCAL_LR, local_momentum = LOCAL_MOMENTUM, labels_dict = LABELS_DICT, device = DEVICE,
attackers_ratio = atr, attack_type=ATTACK_TYPE,
malicious_behavior_rate = MALICIOUS_BEHAVIOR_RATE, rule = RULE,
source_class = SOURCE_CLASS, target_class = TARGET_CLASS,
class_per_peer = CLASS_PER_PEER, samples_per_class = SAMPLES_PER_CLASS,
rate_unbalance = RATE_UNBALANCE, alpha = ALPHA, resume = False)
# In[ ]: