Federated $f$-Differential Privacy

• URL: http://arxiv.org/abs/2102.11158v1
• Date: Mon, 22 Feb 2021 16:28:21 GMT
• Title: Federated $f$-Differential Privacy
• Authors: Qinqing Zheng, Shuxiao Chen, Qi Long, Weijie J. Su
• Abstract summary: Federated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information. We introduce federated $f$-differential privacy, a new notion specifically tailored to the federated setting. We then propose a generic private federated learning framework PriFedSync that accommodates a large family of state-of-the-art FL algorithms.
• Score: 19.499120576896228
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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