Federated Learning Based on Dynamic Regularization

This is implementation of Federated Learning Based on Dynamic Regularization.

Requirements

Please install the required packages. The code is compiled with Python 3.7 dependencies in a virtual environment via

pip install -r requirements.txt

Instructions

Example codes to run FedDyn as well as baseline methods (FedAvg, FedProx and SCAFFOLD) with the synthetic dataset and CIFAR10 is given in example_code_synthetic.py and example_code_cifar10.py.

Generate IID and Dirichlet distributions on various datasets:

CIFAR-10 IID, 100 partitions, balanced data

data_obj = DatasetObject(dataset='CIFAR10', n_client=100, rule='iid', unbalanced_sgm=0)

CIFAR-10 Dirichlet (0.6), 100 partitions, balanced data

data_obj = DatasetObject(dataset='CIFAR10', n_client=100, unbalanced_sgm=0, rule='Dirichlet', rule_arg=0.6)

EMNIST needs to be downloaded from this link.
Shakespeare is generated by using LEAF.

The example codes construct the federated datasets, train methods and plot convergence curve.

Citation

@inproceedings{
acar2021federated,
title={Federated Learning Based on Dynamic Regularization},
author={Durmus Alp Emre Acar and Yue Zhao and Ramon Matas and Matthew Mattina and Paul Whatmough and Venkatesh Saligrama},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=B7v4QMR6Z9w}
}