pFedLA(HeurpFedLA), FedAvg, FedBN

NOTE: This's not the official Repo.

So maybe some hyperparameters not mentioned in paper aren't optimal.

All datasets mentioned in paper are supported(CIFAR10, ...).

If you find sth wrong about the code, feel free to open an issue or pr.

Note that I have recently released a benchmark of federated learning that includes this method and many ohter baselines. Welcome to check my benchmark and star it! 🤗

Run

Generating clients dataset

Segmenting by Dirichlet($\alpha$) and Randomly assigning classes are supported.

E.g.

python ./src/data/run.py --dataset cifar10 --classes 4 --client_num_in_total 10

Check ./src/data/run.py for more Info of all arguments.

Training

Scripts for running pFedLA with fixed args are in ./scripts

cd ./scripts;
chmod +x ./*;
sh ${script}

Also, you can directly run python ./src/server/${algo}.py with your custom arguments after generate clients dataset.

Arguments

Name	Description
k	Blocks retained by client in each round. Specifically for HeurpFedLA.
global_epochs	Communication rounds.
local_epochs	Client local training epochs.
local_lr	Client local optimizer's learning rate.
hn_lr	Learning rate for each client's hypernetwork.
verbose_gap	Logger report training results of selected clients after every verbose_gap communication rounds.
embedding_dim	Size of each client's embedding.
hidden_dim	Size of hidden layers in each client's hypernetwork.
dataset	Used dataset's name.
batch_size	Batch size for local training and test.
valset_ratio	Ratio of validation set.
testset_ratio	Ratio of test set.
gpu	Set as non-zero value for using cuda.
log	Set at non-zero value for saving log file in ./logs .
seed	Random seed for running experiment.
save_period	Temporarily save clients model parameters after every save_period communication rounds.