Title: DFCA: Decentralized Federated Clustering Algorithm
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Federated Learning, Decentralized Federated Learning, Clustered Federated Learning, Communication-Efficient Learning

Abstract: 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 single point of failure, limiting their applicability in more realistic decentralized learning settings. In this work, we introduce DFCA, a fully decentralized clustered Federated Learning 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.

TL;DR: We propose DFCA, a fully decentralized federated clustering algorithm designed to operate effectively in low-connectivity networks with heterogeneous client data distributions.