Experiments_MNIST_EXTREME_NON_IID.ipynb

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