The official code of paper "FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution". "Accepted to FL NeurIPS workshop 2022".
In this repository, we release the official implementation for FLIS algorithms (FLIS-DC, FLIS-HC). The algorithms are evaluated on 4 datasets (Cifar-100/10, Fashion-MNIST, SVHN) with non-iid label distribution skew (noniid-#label2, noniid-#label3, noniid-labeldir).
We provide scripts to run the algorithms, which are put under scripts/. Here is an example to run the script:
cd scripts
bash flis_dc.sh
bash flis_hc.shPlease follow the paper to modify the scripts for more experiments. You may change the parameters listed in the following table.
| The descriptions of parameters are as follows: | Parameter | Description |
|---|---|---|
| ntrials | The number of total runs. | |
| rounds | The number of communication rounds per run. | |
| num_users | The number of clients. | |
| frac | The sampling rate of clients for each round. | |
| local_ep | The number of local training epochs. | |
| local_bs | Local batch size. | |
| lr | The learning rate for local models. | |
| momentum | The momentum for the optimizer. | |
| model | Network architecture. Options: TODO |
|
| dataset | The dataset for training and testing. Options are discussed above. | |
| partition | How datasets are partitioned. Options: homo, noniid-labeldir, noniid-#label1 (or 2, 3, ..., which means the fixed number of labels each party owns). |
|
| datadir | The path of datasets. | |
| logdir | The path to store logs. | |
| log_filename | The folder name for multiple runs. E.g., with ntrials=3 and log_filename=$trial, the logs of 3 runs will be located in 3 folders named 1, 2, and 3. |
|
| alg | Federated learning algorithm. Options are discussed above. | |
| beta | The concentration parameter of the Dirichlet distribution for heterogeneous partition. | |
| local_view | If true puts local test set for each client | |
| gpu | The IDs of GPU to use. E.g., TODO |
|
| print_freq | The frequency to print training logs. E.g., with print_freq=10, training logs are displayed every 10 communication rounds. |
Please cite our work if you find it relavent to your research and used our implementations.
@article{morafah2022flis,
title={FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution},
author={Morafah, Mahdi and Vahidian, Saeed and Wang, Weijia and Lin, Bill},
journal={arXiv preprint arXiv:2208.09754},
year={2022}
}Some parts of our code and implementation has been adapted from NIID-Bench repository.
If you had any questions, please feel free to contact me at mmorafah@eng.ucsd.edu