提出首个抗拜占庭攻击的环形聚合联邦学习方法
Byzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computing
- 采用环形拓扑结构替代中心服务器，实现通信最优
- 理论证明在恶意节点干扰下仍能收敛，且通信效率高
- 适合大规模分布式系统中对安全性和效率要求高的场景
联邦学习（FL）因其数据隐私保护和并行计算加速收敛而受到关注。传统FL依赖中心-客户端（SC）架构，由中心服务器协调多个客户端训练全局模型，但存在服务器通信瓶颈，制约可扩展性。为解决此问题，环形全部归约（RAR）架构被引入，通过消除中心服务器实现带宽最优。然而，RAR环形拓扑的紧密耦合特性使其面临传统SC架构不存在的独特拜占庭攻击风险。尽管潜力巨大，设计具备拜占庭鲁棒性的RAR型联邦学习算法仍是开放问题。本文提出BRACE（Byzantine-robust ring-all-reduce），首个实现拜占庭鲁棒性与通信效率兼备的RAR型联邦学习算法。我们提供了在拜占庭攻击下BRACE收敛性的理论保证，验证了其带宽效率，并通过实验展示了其实际有效性。本工作为设计抗拜占庭攻击的RAR型联邦学习奠定了基础。
原文摘要 · Abstract (English)
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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