Federated learning (FL) enables multiple clients to jointly train a global model under the coordination of a central server. Although FL is a privacy-aware paradigm, where raw data sharing is not required, recent studies have shown that FL might leak the private data of a client through the model parameters shared with the server or the other clients. In this paper, we present the HyFed framework, which enhances the privacy of FL while preserving the utility of the global model. HyFed provides developers with a generic API to develop federated, privacy-preserving algorithms. HyFed supports both simulation and federated operation modes and its source code is publicly available at https://github.com/tum-aimed/hyfed.
@article{arxiv.2105.10545,
title = {HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning},
author = {Reza Nasirigerdeh and Reihaneh Torkzadehmahani and Julian Matschinske and Jan Baumbach and Daniel Rueckert and Georgios Kaissis},
journal= {arXiv preprint arXiv:2105.10545},
year = {2021}
}