HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning
arXiv:2105.10545
Abstract
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.
References in corpus (6)
- Communication-Efficient Learning of Deep Networks from Decentralized Data
- FedML: A Research Library and Benchmark for Federated Machine Learning
- Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
- Privacy-preserving Artificial Intelligence Techniques in Biomedicine
- Flower: A Friendly Federated Learning Research Framework
- Privacy in Distributed Computations based on Real Number Secret Sharing