提出高效去中心化联邦学习系统，通信开销随节点数对数增长。
MAR-FL: A Communication Efficient Peer-to-Peer Federated Learning System
- 基于分组迭代聚合，避免中心协调器
- 通信复杂度降至O(N log N)，较之前降低至对数级
- 适合大规模、不稳定网络环境，支持私有计算
下一代无线系统与分布式机器学习的融合要求联邦学习方法在无线连接的参与者和网络动态变化下仍保持高效与鲁棒。去中心化联邦学习（P2P FL）可消除中心协调瓶颈，但现有方法存在过高的通信复杂度，限制了实际可扩展性。本文提出MAR-FL，一种新型P2P FL系统，通过迭代分组聚合显著降低通信开销，同时保持对网络波动的鲁棒性。MAR-FL的通信成本仅随节点数呈O(N log N)增长，远低于以往基线的O(N²)，在参与节点增多时仍能保持有效性。该系统对不可靠客户端具有韧性，且可集成私有计算能力。
原文摘要 · Abstract (English)
The convergence of next-generation wireless systems and distributed Machine Learning (ML) demands Federated Learning (FL) methods that remain efficient and robust with wireless connected peers and under network churn. Peer-to-peer (P2P) FL removes the bottleneck of a central coordinator, but existing approaches suffer from excessive communication complexity, limiting their scalability in practice. We introduce MAR-FL, a novel P2P FL system that leverages iterative group-based aggregation to substantially reduce communication overhead while retaining resilience to churn. MAR-FL achieves communication costs that scale as O(N log N), contrasting with the O(N^2) complexity of previously existing baselines, and thereby maintains effectiveness especially as the number of peers in an aggregation round grows. The system is robust towards unreliable FL clients and can integrate private computing.
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