提出无需中心服务器的联邦聚类算法，实现去中心化协作训练。
DFCA: Decentralized Federated Clustering Algorithm
- 客户端通过邻居模型平均实现去中心化聚类
- 在稀疏连接下性能接近中心化算法，通信效率高
- 适合动态、不可靠的现实去中心化网络
聚类联邦学习通过将客户端按数据分布相似性分组，有效应对异构数据问题。然而，现有方法如迭代联邦聚类算法（IFCA）依赖中心服务器协调模型更新，形成瓶颈且存在单点故障，限制了其在真实去中心化场景的应用。本文提出完全去中心化的聚类联邦学习算法DFCA，使客户端可在无中心协调下协同训练特定于集群的模型。DFCA采用顺序运行平均机制，随邻居模型更新动态聚合，相比批量聚合更节省通信开销，同时保持聚类性能。实验表明，DFCA在多种数据集上优于其他去中心化算法，且在稀疏连通性条件下表现与中心化IFCA相当，展现出对动态现实去中心化网络的鲁棒性与实用性。
原文摘要 · Abstract (English)
Clustered Federated Learning has emerged as an effective approach for handling heterogeneous data across clients by partitioning them into clusters with similar or identical data distributions. However, most existing methods, including the Iterative Federated Clustering Algorithm (IFCA), rely on a central server to coordinate model updates, which creates a bottleneck and a single point of failure, limiting their applicability in more realistic decentralized learning settings. In this work, we introduce DFCA, a fully decentralized clustered FL algorithm that enables clients to collaboratively train cluster-specific models without central coordination. DFCA uses a sequential running average to aggregate models from neighbors as updates arrive, providing a communication-efficient alternative to batch aggregation while maintaining clustering performance. Our experiments on various datasets demonstrate that DFCA outperforms other decentralized algorithms and performs comparably to centralized IFCA, even under sparse connectivity, highlighting its robustness and practicality for dynamic real-world decentralized networks.
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