Python 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 heteroge...
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 3.8 PyTorch 1.6.0 CUDA 10.1 cuDNN 7.6.5 Currently, this repository supports the following federated learning algorithms: FedAvg (Mcmahan et al. AISTAT 2017): local solver is vanilla SGD; aggregate cumulative local model changes
python3 -u main.py --dataset=$1 --optimizer='fedprox' \ --learning_rate=0.01 --num_rounds=200 --clients_per_round=10 \ --eval_every=1 --batch_size=10 \ --num_epochs=20 \ --drop_percent=$2 \ --model='mclr' \ --mu=$3 ...
All experiments in this paper are conducted on a private cluster with 16 machines connected via Ethernet, each of which is equipped with one NVIDIA TitanX GPU. We treat each machine as one client (worker) in the federated learning setting. ...
python3 -u main.py --dataset=$1 --optimizer='fedavg' \ --learning_rate=0.01 --num_rounds=200 --clients_per_round=10 \ --eval_every=1 --batch_size=10 \ --num_epochs=20 \ --drop_percent=$2 \ --model='mclr' fedprox.sh: ...