Overview
Federated learning enables collaborative model training without sharing raw patient data. It preserves privacy while leveraging diverse datasets. This approach supports multicenter model generalization.
Technical Challenges
Heterogeneous data distributions and communication constraints complicate training. Aggregation strategies and secure protocols address these issues. Model convergence and fairness require careful design.
Clinical Benefits
Federated models generalize better across populations and scanners. They reduce data transfer barriers and legal complexities. Collaborative networks accelerate development of robust AI tools.
Governance
Agreements on data use model updates and validation are essential. Transparency and auditability build trust among partners. Regulatory frameworks evolve to accommodate federated approaches.