Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learning

TL;DR

This paper introduces BASA, a secure aggregation protocol compatible with asynchronous federated learning, enabling privacy-preserving training without synchronous communication or extra hardware, and demonstrating improved efficiency and scalability.

Contribution

The paper presents BASA, the first secure aggregation protocol compatible with asynchronous federated learning that requires only one communication round and no additional hardware.

Findings

BASA outperforms existing protocols in training efficiency.

BASA enhances scalability in cross-device federated learning.

The proposed AFL method ensures secure aggregation without extra hardware.

Abstract

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.