Progress in Applying Federated Learning to Cross-Institutional Medical Data Collaboration

Abstract

The advancement of healthcare informatization has resulted in the exponential growth of patient data stored across various hospitals, laboratories, and clinical centers, properly integration and analysis of this data and use it for machine learning can help make medical processes more efficient. However, privacy regulations and institutional silos pose substantial barriers to collaborative research and centralized model training. Here, Federated Learning (FL) has surfaced as an innovative distributed learning approach, as it empowers institutions to jointly build models while safeguarding raw data privacy. This review outlines FL’s fundamentals and highlights its applications across multiple healthcare domains, including medical image analysis, clinical outcome prediction, and wearable health monitoring. The fundamental FL designs (horizontal FL, vertical FL, and split FL learning) and privacy-enhancing methods (safe aggregation, homomorphic encryption, and differential privacy) are discussed. Additionally, we also examine recent advances in adaptive privacy mechanisms, asynchronous updates and explainable AI to support clinical integration. The study concludes with a discussion of current limitations and future research directions, such as multimodal FL, personalized modeling, and edge-based computing.