Asynchronous federated learning (AFL) is an effective method to address the challenge of device heterogeneity in cross-device federated learning. However, AFL is usually incompatible with existing secure aggregation protocols used to protect user privacy in federated learning because most existing secure aggregation protocols are based on synchronous aggregation. To address this problem, we propose a novel secure aggregation protocol named buffered asynchronous secure aggregation (BASA) in this paper. Compared with existing protocols, BASA is fully compatible with AFL and provides secure aggregation under the condition that each user only needs one round of communication with the server without relying on any synchronous interaction among users. Based on BASA, we propose the first AFL method which achieves secure aggregation without extra requirements on hardware. We empirically demonstrate that BASA outperforms existing secure aggregation protocols for cross-device federated learning in terms of training efficiency and scalability.

翻译：异步联邦学习（AFL）是解决跨设备联邦学习中设备异构性挑战的一种有效方法。然而，AFL通常与联邦学习中用于保护用户隐私的现有安全聚合协议不兼容，因为大多数现有安全聚合协议基于同步聚合。为解决此问题，本文提出了一种新颖的安全聚合协议，称为缓冲式异步安全聚合（BASA）。与现有协议相比，BASA完全兼容AFL，并在每个用户仅需与服务器进行一轮通信、且不依赖用户间任何同步交互的条件下提供安全聚合。基于BASA，我们提出了首个实现安全聚合且无需额外硬件要求的AFL方法。我们通过实验证明，在训练效率和可扩展性方面，BASA优于现有的跨设备联邦学习安全聚合协议。