mlsanityData to 14 Oct 2026

MAR-FL: A Communication Efficient Peer-to-Peer Federated Learning System

arXiv 2512.05234 ↗First posted 4 Dec 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

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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References · 14

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  10. 2021Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devicescited by 36
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  12. 2020Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selectioncited by 20
2 more
  1. 2020GFL: A Decentralized Federated Learning Framework Based On Blockchaincited by 12
  2. 2019Performance Analysis and Comparison of Distributed Machine Learning Systemscited by 9

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