fedconcat's Introduction

FedConcat

This is source code for Exploiting Label Skew in Federated Learning with Model Concatenation.

An example running script is in run.sh.

Parameter	Description
`model`	The model architecture. Options: `simple-cnn`, `vgg`, `resnet`, `mlp`. Default = `mlp`.
`dataset`	Dataset to use. Options: `mnist`, `cifar10`, `fmnist`, `svhn`, `generated`, `femnist`, `a9a`, `rcv1`, `covtype`. Default = `mnist`.
`alg`	The training algorithm. Options: `fedconcat`.
`lr`	Learning rate for the local models, default = `0.01`.
`batch-size`	Batch size, default = `64`.
`epochs`	Number of local training epochs, default = `5`.
`n_parties`	Number of parties, default = `2`.
`rho`	The parameter controlling the momentum SGD, default = `0`.
`n_clusters`	Number of clusters.
`encoder_round`	Number of communication rounds for training encoders in each cluster.
`classifier_round`	Number of communication rounds for training the global classifier.
`partition`	The partition way. Options: `homo`, `noniid-labeldir`, `noniid-#label1` (or 2, 3, ..., which means the fixed number of labels each party owns)
`beta`	The concentration parameter of the Dirichlet distribution for heterogeneous partition, default = `0.5`.
`device`	Specify the device to run the program, default = `cuda:0`.
`datadir`	The path of the dataset, default = `./data/`.
`logdir`	The path to store the logs, default = `./logs/`.
`init_seed`	The initial seed, default = `0`.

Citation

If you find this repository useful, please cite our paper:

@article{diao2023exploiting,
  title={Exploiting Label Skews in Federated Learning with Model Concatenation},
  author={Diao, Yiqun and Li, Qinbin and He, Bingsheng},
  journal={arXiv preprint arXiv:2312.06290},
  year={2023}
}

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