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main.py
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#!/usr/bin/env python
from comet_ml import Experiment
import h5py
import matplotlib.pyplot as plt
import numpy as np
import argparse
import importlib
import random
import os
from FLAlgorithms.servers.serveravg import FedAvg
from FLAlgorithms.servers.serverpFedMe import pFedMe
from FLAlgorithms.servers.serverperavg import PerAvg
from FLAlgorithms.servers.serverFedU import FedU
from FLAlgorithms.servers.serverScaffold import SCAFFOLD
from FLAlgorithms.servers.serverfedprox import FedProx
from FLAlgorithms.servers.serverAFL import FedAFL
from FLAlgorithms.servers.serverAPFL import FedAPFL
from FLAlgorithms.servers.serverfedrep import FedREP
from FLAlgorithms.servers.serverlocal import FedLocal
from FLAlgorithms.servers.serverglobal import FedGlobal
from utils.model_utils import read_data
from FLAlgorithms.trainmodel.models import *
from utils.plot_utils import *
import torch
from utils.options import args_parser
# import comet_ml at the top of your file
# Create an experiment with your api key:
def main(experiment, dataset, algorithm, model, batch_size, learning_rate, beta, L_k, num_glob_iters,
local_epochs, optimizer, numusers, K, personal_learning_rate, times, commet, gpu, cutoff):
# Get device status: Check GPU or CPU
device = torch.device("cuda:{}".format(gpu) if torch.cuda.is_available() and gpu != -1 else "cpu")
data = read_data(dataset) , dataset
for i in range(times):
print("---------------Running time:------------",i)
# Generate model\
torch.manual_seed(0)
if(model == "mclr"):
if(dataset == "human_activity"):
model = Mclr_Logistic(561,6).to(device), model
elif(dataset == "gleam"):
model = Mclr_Logistic(561,6).to(device), model
elif(dataset == "vehicle_sensor"):
model = Mclr_Logistic(100,2).to(device), model
elif(dataset == "Synthetic"):
model = Mclr_Logistic(60,10).to(device), model
elif(dataset == "EMNIST"):
model = Mclr_Logistic(784,62).to(device), model
elif(dataset == "Synthetic"):
model = Mclr_Logistic(60,10).to(device), model
else:#(dataset == "Mnist"):
model = Mclr_Logistic().to(device), model
elif(model == "dnn"):
if(dataset == "human_activity"):
model = DNN(561,100,12).to(device), model
elif(dataset == "gleam"):
model = DNN(561,20,6).to(device), model
elif(dataset == "vehicle_sensor"):
model = DNN(100,20,2).to(device), model
elif(dataset == "Synthetic"):
model = DNN(60,20,10).to(device), model
elif(dataset == "EMNIST"):
model = DNN(784,200,62).to(device), model
else:#(dataset == "Mnist"):
model = DNN2().to(device), model
elif(model == "cnn"):
if(dataset == "Cifar10"):
model = CNNCifar(10).to(device), model
else:
return
# select algorithm
if(algorithm == "FedAvg"):
if(commet):
experiment.set_name(dataset + "_" + algorithm + "_" + model[1] + "_" + str(batch_size) + "_" + str(learning_rate) + "_" + str(num_glob_iters) + "_"+ str(local_epochs) + "_"+ str(numusers))
server = FedAvg(experiment, device, data, algorithm, model, batch_size, learning_rate, beta, L_k, num_glob_iters, local_epochs, optimizer, numusers, i, cutoff)
elif(algorithm == "SCAFFOLD"):
if(commet):
experiment.set_name(dataset + "_" + algorithm + "_" + model[1] + "_" + str(batch_size) + "_" + str(learning_rate) + "_" + str(num_glob_iters) + "_"+ str(local_epochs) + "_"+ str(numusers))
server = SCAFFOLD(experiment, device, data, algorithm, model, batch_size, learning_rate, beta, L_k, num_glob_iters, local_epochs, optimizer, numusers, i, cutoff)
elif(algorithm == "FedProx"):
if(commet):
experiment.set_name(dataset + "_" + algorithm + "_" + model[1] + "_" + str(batch_size) + "_" + str(learning_rate) + "_" + str(num_glob_iters) + "_"+ str(local_epochs) + "_"+ str(numusers))
server = FedProx(experiment, device, data, algorithm, model, batch_size, learning_rate, beta, L_k, num_glob_iters, local_epochs, optimizer, numusers, i, cutoff)
elif(algorithm == "PerAvg"):
if(commet):
experiment.set_name(dataset + "_" + algorithm + "_" + model[1] + "_" + str(batch_size) + "_" + str(learning_rate) + "_" + str(personal_learning_rate) + "_" + str(learning_rate)+ "_" + str(num_glob_iters) + "_"+ str(local_epochs) + "_"+ str(numusers))
server = PerAvg(experiment, device, data, algorithm, model, batch_size, learning_rate, beta, L_k, num_glob_iters, local_epochs, optimizer, numusers, i, cutoff)
elif(algorithm == "FedU"):
if(commet):
experiment.set_name(dataset + "_" + algorithm + "_" + model[1] + "_" + str(batch_size) + "_" + str(learning_rate)+ "_" + str(L_k) + "L_K"+ "_" + str(num_glob_iters) + "_"+ str(local_epochs) + "_"+ str(numusers))
server = FedU(experiment, device, data, algorithm, model, batch_size, learning_rate, beta, L_k, num_glob_iters, local_epochs, optimizer, numusers, K, i, cutoff)
elif(algorithm == "pFedMe"):
if(commet):
experiment.set_name(dataset + "_" + algorithm + "_" + model[1] + "_" + str(batch_size) + "_" + str(learning_rate) + "_" + str(personal_learning_rate) + "_" + str(num_glob_iters) + "_"+ str(local_epochs) + "_"+ str(numusers))
server = pFedMe(experiment, device, data, algorithm, model, batch_size, learning_rate, beta, L_k, num_glob_iters, local_epochs, optimizer, numusers, K, personal_learning_rate, i, cutoff)
elif(algorithm == "FedAFL"):
if(commet):
experiment.set_name(dataset + "_" + algorithm + "_" + model[1] + "_" + str(batch_size) + "_" + str(learning_rate) + "_" + str(personal_learning_rate) + "_" + str(num_glob_iters) + "_"+ str(local_epochs) + "_"+ str(numusers))
server = FedAFL(experiment, device, data, algorithm, model, batch_size, learning_rate, beta, L_k, num_glob_iters, local_epochs, optimizer, numusers, i, cutoff)
elif(algorithm == "APFL"):
if(commet):
experiment.set_name(dataset + "_" + algorithm + "_" + model[1] + "_" + str(batch_size) + "_" + str(learning_rate) + "_" + str(personal_learning_rate) + "_" + str(num_glob_iters) + "_"+ str(local_epochs) + "_"+ str(numusers))
server = FedAPFL(experiment, device, data, algorithm, model, batch_size, learning_rate, beta, L_k, num_glob_iters, local_epochs, optimizer, numusers, i, cutoff)
elif(algorithm == "FedRep"):
if(commet):
experiment.set_name(dataset + "_" + algorithm + "_" + model[1] + "_" + str(batch_size) + "_" + str(learning_rate) + "_" + str(personal_learning_rate) + "_" + str(num_glob_iters) + "_"+ str(local_epochs) + "_"+ str(numusers))
server = FedREP(experiment, device, data, algorithm, model, batch_size, learning_rate, beta, L_k, num_glob_iters, local_epochs, optimizer, numusers, i, cutoff)
elif(algorithm == "Local"):
if(commet):
experiment.set_name(dataset + "_" + algorithm + "_" + model[1] + "_" + str(batch_size) + "_" + str(learning_rate) + "_" + str(L_k) + "_" + str(num_glob_iters) + "_"+ str(local_epochs) + "_"+ str(numusers))
server = FedLocal(experiment, device, data, algorithm, model, batch_size, learning_rate, beta, L_k, num_glob_iters, local_epochs, optimizer, numusers, i, cutoff)
elif(algorithm == "Global"):
if(commet):
experiment.set_name(dataset + "_" + algorithm + "_" + model[1] + "_" + str(batch_size) + "_" + str(learning_rate) + "_" + str(L_k) + "_" + str(num_glob_iters) + "_"+ str(local_epochs) + "_"+ str(numusers))
server = FedGlobal(experiment, device, data, algorithm, model, batch_size, learning_rate, beta, L_k, num_glob_iters, local_epochs, optimizer, numusers, i, cutoff)
else:
print("Algorithm is invalid")
return
server.train()
server.test()
average_data(num_users=numusers, loc_ep1=local_epochs, Numb_Glob_Iters=num_glob_iters, lamb=L_k,learning_rate=learning_rate, beta = beta, algorithms=algorithm, batch_size=batch_size, dataset=dataset, k = K, personal_learning_rate = personal_learning_rate,times = times, cutoff = cutoff)
if __name__ == "__main__":
args = args_parser()
print("=" * 80)
print("Summary of training process:")
print("Algorithm: {}".format(args.algorithm))
print("Batch size: {}".format(args.batch_size))
print("Learing rate : {}".format(args.learning_rate))
print("Average Moving : {}".format(args.beta))
print("Subset of users : {}".format(args.subusers))
print("Number of global rounds : {}".format(args.num_global_iters))
print("Number of local rounds : {}".format(args.local_epochs))
print("Dataset : {}".format(args.dataset))
print("Local Model : {}".format(args.model))
print("=" * 80)
if(args.commet):
# Create an experiment with your api key:
experiment = Experiment(
api_key="VtHmmkcG2ngy1isOwjkm5sHhP",
project_name="multitask-for-test",
workspace="federated-learning-exp",
)
hyper_params = {
"dataset":args.dataset,
"algorithm" : args.algorithm,
"model":args.model,
"batch_size":args.batch_size,
"learning_rate":args.learning_rate,
"beta" : args.beta,
"L_k" : args.L_k,
"num_glob_iters":args.num_global_iters,
"local_epochs":args.local_epochs,
"optimizer": args.optimizer,
"numusers": args.subusers,
"K" : args.K,
"personal_learning_rate" : args.personal_learning_rate,
"times" : args.times,
"gpu": args.gpu,
"cut-off": args.cutoff
}
experiment.log_parameters(hyper_params)
else:
experiment = 0
main(
experiment= experiment,
dataset=args.dataset,
algorithm = args.algorithm,
model=args.model,
batch_size=args.batch_size,
learning_rate=args.learning_rate,
beta = args.beta,
L_k = args.L_k,
num_glob_iters=args.num_global_iters,
local_epochs=args.local_epochs,
optimizer= args.optimizer,
numusers = args.subusers,
K=args.K,
personal_learning_rate=args.personal_learning_rate,
times = args.times,
commet = args.commet,
gpu=args.gpu,
cutoff = args.cutoff
)