repo in several methods FedAvg, FedMD, FedProto, FedProx, FedHKD.
- main.py //For training the model
- models.py //Our VAEs model for FMNIST, CIFAR10/100
- sampling.py // functions that generate non-iid datasets for federated learning
- utils.py // define functions that compute accuracy, soft prediction and model averaging
- mem_utils.py // Library for monitoring memory usage and training time
- option.py // define hyper-parameters
- Server/*.py // object definition for server in each method
- Client/*.py // object definition for client in each method
- --dataset: 'CIFAR10', 'CIFAR100', ' SVHN'
- --batch_size: 64 by defalut
- --num_epochs: number of global rounds, 50 by defalut
- --lr: learning rate, 0.001 by defalut
- --lr_sh_rate: period of learning rate decay, 10 by defalut
- --dropout_rate: drop out rate for each layer, 0.2 by defalut
- --clip_grad: maximum norm for gradient
- --num_users: number of clients, 10 by defalut
- --sampling_rate: proportion of clients send updates per round, 1 by defalut
- --local_ep: local epoch, 5 by defalut
- --beta: concentration parameter for Dirichlet distribution: 0.5 by defalut
- --seed: random seed(for better reproducting experiments): 0 by defalut
- --std: standard deviation by Differential Noise, 2 by defalut
- --code_len: length of latent vector, 32 by defalut
- --alg: 'FedAvg, FedProx, Moon, FedMD, Fedproto, FedHKD'
- --eval_only: only ouput the testing accuracy during training and the running time
- --part: percentage of each local data
- --temp: temperture for soft prediction
- --lam: hyper-parameter for loss2
- --gamma: hyper-parameter for loss3
- --model: CNN resnet18 shufflenet
- --save_model: save checkpoints of the model
python main.py