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arXiv 2406.19002cs.ITmath.IT

补充文件:面向半去中心化联邦学习的编码协作网络

Supplementary File: Coded Cooperative Networks for Semi-Decentralized Federated Learning

Shudi Weng, Ming Xiao, Chao Ren, Mikael Skoglund

AI总结:

针对半去中心化联邦学习的落后节点问题,提出无需全网先验信息、利用无线分集的确定性编码协作网络,并给出中断与收敛速率分析及仿真验证。

摘要:

为增强联邦学习(FL)系统对落后节点的韧性,近期已有研究提出一种半去中心化方法,使客户端之间能够协作。不同于现有根据网络拓扑自适应调整协作权重的半去中心化方案,本文提出一种确定性编码网络,利用无线分集实现半去中心化FL,且无需有关整个网络的先验信息。此外,本文给出了所提方案的中断与收敛速率理论分析。最后,全面仿真结果证明,所提方法优于各基准方法。

英文摘要:

To enhance straggler resilience in federated learning (FL) systems, a semi-decentralized approach has been recently proposed, enabling collaboration between clients. Unlike the existing semi-decentralized schemes, which adaptively adjust the collaboration weight according to the network topology, this letter proposes a deterministic coded network that leverages wireless diversity for semi-decentralized FL without requiring prior information about the entire network. Furthermore, the theoretical analyses of the outage and the convergence rate of the proposed scheme are provided. Finally, the superiority of our proposed method over benchmark methods is demonstrated through comprehensive simulations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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