Federated Learning for Privacy-Preserving Data Sharing in Healthcare

Abstract

Data privacy is a major concern in healthcare applications involving sensitive patient information. This paper investigates the use of federated learning to enable collaborative model training without sharing raw data. The proposed framework allows multiple healthcare institutions to train a shared model while maintaining data confidentiality. Experimental results show comparable performance to centralized models, with enhanced privacy protection. The study highlights federated learning as a promising approach for secure and collaborative healthcare analytics.