Federated f-Differential Privacy

• Published in: Proceedings of machine learning research Vol. 130; pp. 2251 - 2259
• Main Authors: Zheng, Qinqing, Chen, Shuxiao, Long, Qi, Su, Weijie J
• Format: Journal Article
• Language: English
• Published: 01.04.2021
• ISSN: 2640-3498

Federated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information without compromising much on the privacy of their local sensitive data. In this paper, we introduce federated f-differential privacy , a new notion specifically tailored to the federated setting, based on the framework of Gaussian differential privacy. Federated f -differential privacy operates on record level : it provides the privacy guarantee on each individual record of one client’s data against adversaries. We then propose a generic private federated learning framework PriFedSync that accommodates a large family of state-of-the-art FL algorithms, which provably achieves federated f -differential privacy. Finally, we empirically demonstrate the trade-off between privacy guarantee and prediction performance for models trained by PriFedSync in computer vision tasks.