MedFedSSL: Semi-Supervised Federated Learning with Dual Consistency and Adaptive Distillation for Medical Imaging

Medical image analysis presents unique challenges due to limited labeled data, privacy concerns, and heterogeneous data distributions across institutions. Federated learning FL offers a promising solution by enabling collaborative model training without sharing raw data. However, existing FL approaches often struggle with label scarcity in medical domains. In this paper, we propose MedFedSSL, a novel semi-supervised federated learning framework specifically designed for medical image analysis. Our approach integrates a dual-consistency regularization mechanism with an adaptive knowledge distillation strategy to effectively leverage both labeled and unlabeled data across distributed clients. We introduce a theoretically sound optimization objective that addresses the challenges of data heterogeneity and label imbalance in medical imaging. Extensive experiments on multiple medical imaging datasets demonstrate that MedFedSSL significantly outperforms state-of-the-art federated learning and semi-supervised learning methods, achieving superior performance with limited labeled data while preserving privacy. Our theoretical analysis provides convergence guarantees and bounds on the generalization error of the proposed approach.