Federated Learning for Privacy-Preserving Medical Data Sharing: Utilizes federated learning techniques to enable privacy-preserving sharing of medical data across healthcare institutions

Abstract

Federated learning has emerged as a promising approach for privacy-preserving sharing of medical data across healthcare institutions. This paper presents a comprehensive overview of federated learning techniques and their application in the healthcare domain. We discuss the challenges and opportunities of federated learning in medical data sharing, including privacy concerns, data heterogeneity, and model aggregation. We also review existing frameworks and protocols for federated learning in healthcare and propose a novel approach to enhance the privacy and security of medical data sharing. Our experimental results demonstrate the effectiveness of federated learning in preserving privacy while enabling collaborative learning on medical datasets. Overall, this paper highlights the potential of federated learning to revolutionize medical data sharing by addressing privacy concerns and enabling seamless collaboration among healthcare institutions.