DFCA: Decentralized Federated Clustering Algorithm

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, typically requiring stable connectivity, synchronous communication rounds, and global aggregation of client models. These assumptions are difficult to satisfy in decentralized and heterogeneous environments, where clients may only have limited, local communication with a small subset of peers. As a result, such methods create a bottleneck and a single point of failure, limiting their applicability in realistic decentralized learning settings. This limitation is particularly severe in Internet of Things settings, where large numbers of resource-constrained devices, intermittent or sparse connectivity, and dynamic participation make reliance on a central server impractical. In this work, we introduce the Decentralized Federated Clustering Algorithm (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.