FedPC: An Efficient Prototype-Based Clustered Federated Learning on Medical Imaging

Federated learning (FL) has emerged as a promising distributed paradigm that enables collaborative model training while preserving data privacy, but it suffers from performance degradation due to data heterogeneity. Although clustered federated learning (CFL) attempts to address this challenge by grouping clients with similar data distributions, existing methods are inefficient in capturing client data representations, leading to incorrect cluster identities and inferior cluster performance. To overcome these limitations, we propose an efficient prototype-based CFL framework (FedPC). Specifically, we introduce a dual-prototype strategy combining specific prototypes and generalized prototypes to capture class representations for cluster identities, along with a prototype-contrastive training mechanism that maximizes intra-cluster prototype consistency to improve cluster performance. Extensive experiments on medical imaging datasets (BloodMNIST and DermaMNIST) demonstrate that the FedPC outperforms nine state-of-the-art (SOTA) approaches, achieving average improvements of 2.17% and 3.47%, respectively. Furthermore, the FedPC reduces communication overhead by 3.33 to 5.68 times compared to SOTA methods, showcasing its efficiency in real-world FL scenarios.