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.

翻译：本文提出了一种新型集中式异步联邦学习框架FAVANO，用于在资源受限环境中训练深度神经网络。尽管经典联邦学习广受欢迎，但在大型无线网络上扩展同步通信的难度日益增加。此外，客户端通常具有不同的计算资源与计算速度，这会在异步更新时导致显著的偏差（偏向于"快速"客户端）。因此，联邦学习的实际部署需要处理通信/资源受限场景下计算速度差异巨大的用户。我们在光滑非凸环境下证明了FAVANO的收敛保证，并与现有边界进行了细致对比。实验结果表明，FAVANO算法在标准基准测试中优于当前方法。