This is implementation of Federated Learning Based on Dynamic Regularization.
Please install the required packages. The code is compiled with Python 3.7 dependencies in a virtual environment via
pip install -r requirements.txt
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.
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.
@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}
}