hansomesong / FedConcat

Exploiting Label Skew in Federated Learning with Model Concatenation (AAAI 2024)

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}
}