Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learning
This addresses privacy protection in cross-device federated learning for heterogeneous devices, though it is incremental as it adapts secure aggregation to an existing AFL framework.
The paper tackles the incompatibility between asynchronous federated learning (AFL) and existing secure aggregation protocols by proposing BASA, a novel protocol that enables secure aggregation in AFL with one round of communication per user, improving training efficiency and scalability.
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.