In this paper, we propose a novel centralized Asynchronous Federated Learning (FL) framework, FAVAS, 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 FAVAS 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 FAVAS algorithm outperforms current methods on standard benchmarks.

翻译：本文提出了一种新型集中式异步联邦学习框架FAVAS，用于在资源受限环境下训练深度神经网络。尽管经典联邦学习广受欢迎，但其在大规模无线网络上同步通信的扩展性正面临日益严峻的挑战。此外，客户端通常具有不同的计算资源和计算速度，这可能导致异步更新时产生显著偏差（偏向"快速"客户端）。因此，联邦学习的实际部署需要处理通信/资源受限条件下计算速度差异较大的用户。我们为FAVAS在光滑非凸环境下提供了收敛性保证，并在现有收敛界可用时进行了细致比较。实验结果表明，FAVAS算法在标准基准测试上优于现有方法。