Kavli Affiliate: Jia Liu
| First 5 Authors: Minghong Fang, Zhuqing Liu, Xuecen Zhao, Jia Liu,
| Summary:
Federated learning (FL) has gained attention as a distributed learning
paradigm for its data privacy benefits and accelerated convergence through
parallel computation. Traditional FL relies on a server-client (SC)
architecture, where a central server coordinates multiple clients to train a
global model, but this approach faces scalability challenges due to server
communication bottlenecks. To overcome this, the ring-all-reduce (RAR)
architecture has been introduced, eliminating the central server and achieving
bandwidth optimality. However, the tightly coupled nature of RAR’s ring
topology exposes it to unique Byzantine attack risks not present in SC-based
FL. Despite its potential, designing Byzantine-robust RAR-based FL algorithms
remains an open problem. To address this gap, we propose BRACE
(Byzantine-robust ring-all-reduce), the first RAR-based FL algorithm to achieve
both Byzantine robustness and communication efficiency. We provide theoretical
guarantees for the convergence of BRACE under Byzantine attacks, demonstrate
its bandwidth efficiency, and validate its practical effectiveness through
experiments. Our work offers a foundational understanding of Byzantine-robust
RAR-based FL design.
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