Federated Multi-Institutional Learning Framework for Secure Healthcare Data Analytics

Abstract

The growing need for collaborative healthcare analytics across institutions is often restricted by strict data privacy regulations and concerns over sensitive patient information. This paper presents a Federated Multi-Institutional Learning Framework for Secure Healthcare Data Analytics that enables distributed model training without sharing raw data. The proposed system allows multiple healthcare organizations to collaboratively train machine learning models by sharing only model updates instead of actual patient data. Advanced techniques such as secure aggregation and differential privacy are incorporated to ensure data confidentiality and protection against inference attacks. The framework supports heterogeneous data environments and enables scalability across diverse healthcare systems. Deep learning models are employed to extract meaningful patterns from distributed datasets while maintaining high predictive performance. Experimental results demonstrate that the proposed approach achieves comparable accuracy to centralized models while preserving privacy and reducing communication overhead. Additionally, integration with prior research in healthcare analytics enhances system robustness and generalization. The study highlights the potential of federated learning in enabling secure, collaborative, and scalable healthcare data analytics across multiple institutions.