FAVANO: Federated AVeraging with Asynchronous NOdes

• URL: http://arxiv.org/abs/2305.16099v2
• Date: Wed, 22 Nov 2023 19:52:37 GMT
• Title: FAVANO: Federated AVeraging with Asynchronous NOdes
• Authors: Louis Leconte, Van Minh Nguyen, Eric Moulines
• Abstract summary: We propose a novel centralized Asynchronous Federated Learning (FL) framework, FAVANO, for training Deep Neural Networks (DNNs) in resource constrained environments.
• Score: 14.412305295989444
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a novel centralized Asynchronous Federated Learning (FL) framework, FAVANO, for training Deep Neural Networks (DNNs) in resource-constrained environments. Despite its popularity, ``classical'' federated learning faces the increasingly difficult task of scaling synchronous communication over large wireless networks. Moreover, clients typically have different computing resources and therefore computing speed, which can lead to a significant bias (in favor of ``fast'' clients) when the updates are asynchronous. Therefore, practical deployment of FL requires to handle users with strongly varying computing speed in communication/resource constrained setting. We provide convergence guarantees for FAVANO in a smooth, non-convex environment and carefully compare the obtained convergence guarantees with existing bounds, when they are available. Experimental results show that the FAVANO algorithm outperforms current methods on standard benchmarks.

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