Federated Learning for Privacy-Preserving AI in Decentralized Healthcare Systems

This research explores the application of federated learning to enable robust AI model training across distributed healthcare data sources without compromising patient privacy. Our method demonstrates superior performance in diagnosing rare diseases and predicting treatment outcomes while adhering to stringent data protection regulations, offering a scalable solution for collaborative medical AI.