FAVANO: Federated Averaging with Asynchronous Nodes

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. Experimental results show that the FAVANO algorithm outperforms current methods on standard benchmarks.