Federated learning framework for medical image analysis with perspective-aware contrastive and mixture of experts

Medical image analysis faces persistent challenges due to the distributed data, limited annotations, and variations in imaging modalities, acquisition protocols, and patient demographics. Centralized deep learning approaches compromise data privacy, while Federated Learning (FL) enables decentralized model training without sharing raw data. However, conventional FL frameworks struggle with non-IID distributions and heterogeneous clinical environments, limiting their generalization and stability.