Federated Learning in Medical AI: Advancing Privacy-Preserving Data Sharing for Collaborative Healthcare Research

Federated Learning (FL) has emerged as a practical approach to training machine learning models collaboratively across multiple institutions, especially in domains like healthcare where patient data is highly sensitive. By allowing data to remain local while only model updates are shared, FL addresses a critical balance between innovation and privacy. This paper explores FL’s growing relevance in medical AI particularly its role in improving diagnostic models, patient management, and regulatory compliance. Key contributions include a breakdown of FL's interaction with healthcare systems, a look at privacy-preserving techniques like differential privacy and homomorphic encryption, and real-world use cases in oncology, cardiology, and radiology. We present experimental results, challenges with interoperability, and a vision for FL's evolution in secure global healthcare collaboration