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federated learning (FL) enables decentralized devices to collaboratively train models without sharing raw data. However, when client data is highly heterogeneous, conventional FL often suffers from poor model performance and personalization. To address this, clustered FL (CFL) has emerged as a promising solution, grouping clients with similar data distributions to jointly learn better personalized models. Yet, most existing CFL methods rely on predefined thresholds or fixed numbers of clusters, which limits their adaptability to real-world, dynamic environments with diverse and evolving client data. This study introduces FedCDC, a novel CFL framework that leverages graph clustering to dynamically identify client communities without prior knowledge of clustering structure. Specifically, we propose a client similarity identification algorithm based on Louvain community detection community detection clustering (CDC), which constructs a similarity graph using model inference results and performs modularity-optimizing clustering. Furthermore, the graph-based approach captures high-order structural relationships among clients, enabling more precise and stable clustering even under severe data heterogeneity conditions. Extensive experiments across some benchmark datasets demonstrate that FedCDC consistently outperforms state-of-the-art (SOTA) baselines. In the challenging Dir(0.1) setting on CIFAR-100, FedCDC achieves accuracy gains of 12.75% over FedAvg, 23.33% over PerFedAvg, and 3.42% over FLIS(DC). More broadly, this work bridges graph theory with FL, introducing a scalable and interpretable way to form client communities. It provides a solution for real-world deployments of FL systems that are both accurate and adaptive, particularly in complex environments.
Sun et al. (Tue,) studied this question.