DIFFERENTIALLY PRIVATE FEDERATED FRANK-WOLFE

Francis, R and Chepuri, SP (2024) DIFFERENTIALLY PRIVATE FEDERATED FRANK-WOLFE. In: International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024, 14 -19 April 2024, Seoul, pp. 7395-7399.

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Abstract

In this paper, we propose DP-FedFW, a novel Frank-Wolfe based federated learning algorithm with local (ϵ, δ)-differential privacy (DP) guarantees in a constrained learning setting. In DP-FedFW, we perturb local models to ensure privacy while communicating with the server, and each client performs several Frank-Wolfe steps to arrive at a local model. The proposed method guarantees (ϵ, δ)-DP for each client and has a sublinear convergence of O(1/k) for smooth convex objective functions, where k is the number of communication rounds and an asymptotic convergence for smooth non-convex objective functions. The theoretical analysis shows that given an (ϵ, δ)-DP requirement, the proposed algorithm's performance improves with the number of clients and the batch size. We empirically validate the efficacy of the proposed method on several constrained machine learning tasks. © 2024 IEEE.

Item Type:	Conference Paper
Publication:	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	19 Aug 2024 13:26
Last Modified:	19 Aug 2024 13:26
URI:	http://eprints.iisc.ac.in/id/eprint/85478

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