  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.
