对比两种联邦学习通信方式，发现数据异质性决定哪种更优。
A Unified Convergence Analysis for Semi-Decentralized Learning: Sampled-to-Sampled vs. Sampled-to-All Communication
- 统一框架分析采样到采样与采样到全网通信机制
- 数据异质性高时采样到全网更优，低时采样到采样更稳
- 给出实际部署中通信策略选择的明确指导
在半去中心化联邦学习中，设备主要通过设备间通信，但定期有部分设备将本地模型上传至服务器，由服务器聚合。服务器可将聚合模型仅发送给被采样的设备（采样到采样，S2S），或广播给所有设备（采样到全网，S2A）。尽管二者均有实际价值，但缺乏严谨的理论与实证比较。本文通过统一收敛框架，分析采样率、服务器聚合频率与网络连通性等关键参数的影响。理论与实验结果揭示：在设备间数据异质性较高时，S2A表现更优；异质性较低时，S2S更具稳定性。研究为实际半去中心化联邦学习系统提供了明确的通信策略设计依据。
原文摘要 · Abstract (English)
In semi-decentralized federated learning, devices primarily rely on device-to-device communication but occasionally interact with a central server. Periodically, a sampled subset of devices uploads their local models to the server, which computes an aggregate model. The server can then either (i) share this aggregate model only with the sampled clients (sampled-to-sampled, S2S) or (ii) broadcast it to all clients (sampled-to-all, S2A). Despite their practical significance, a rigorous theoretical and empirical comparison of these two strategies remains absent. We address this gap by analyzing S2S and S2A within a unified convergence framework that accounts for key system parameters: sampling rate, server aggregation frequency, and network connectivity. Our results, both analytical and experimental, reveal distinct regimes where one strategy outperforms the other, depending primarily on the degree of data heterogeneity across devices. These insights lead to concrete design guidelines for practical semi-decentralized FL deployments.
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