def client_train_fedavg(data_ref, model, config, calculator): if config['parallel_type'] != 'obj': data = fus.sharable2dataset(data_ref) else: data = data_ref device = calculator.device data_loader = tud.DataLoader(data, batch_size=config['batch_size'], shuffle=True) optimizer = cal...
PyTorch implementation of Federated Learning algorithms FedSGD, FedAvg, FedAvgM, FedIR, FedVC, FedProx and standard SGD, applied to visual classification. Client distributions are synthesized with arbitrary non-identicalness and imbalance (Dirichlet priors). Client systems can be arbitrarily heterogeneous....
python main.py --FL fedavg --train_bs 50 --train_ep 5 --epoch 500 --non_alpha 0.5 --model lenet --dataset cifar_LDA --num_selected 10 --num_clients 100 About Federated Learning Algorithm (Pytorch) : FedAvg, FedProx, MOON, SCAFFOLD, FedDyn Resources Readme Activity Stars 18 ...
python main.py --all_clients \ --fed fedavg \ --gpu 0 \ --seed 1 \ --sampling noniid \ --sys_homo \ --num_channels 3 \ --dataset cifar Explanations of arguments: fed: federated optimization algorithm mu: parameter for fedprox ...