FEDERATED LEARNING FOR PRIVACY- PRESERVING HEALTH DATA ANALYSIS IN INDIAN HOSPITALS

Abstract

The digital transformation of healthcare in India has resulted in the rapid growth of Electronic Health Records (EHRs) across hospitals [1][3], paving the way for advanced data-driven clinical decision support systems [2][11]. However, centralized machine learning models require aggregating patient data into a single repository, which raises major concerns about privacy, data ownership, regulatory compliance, and cyber-security risks [4][9][13][14]. To address these challenges, this research proposes a federated learning-based privacy-preserving health data analysis framework for Indian hospitals [3][8]. In the proposed system, deep learning models are trained locally within each hospital without transferring raw patient data to external servers [1][12]. Instead, only encrypted model parameters or gradients are communicated to a central coordinator for secure aggregation [7][20], ensuring that sensitive medical data never leaves hospital premises [4][24]. The federated learning approach enables collaborative model development across multiple hospitals while maintaining data sovereignty, eliminating the risks of data leakage or unauthorized access [3][15]. The framework is enhanced with secure aggregation, differential privacy, and homomorphic encryption [6][10], making it resilient to inference attacks and modelreconstruction threats [16][24]. Experiments conducted over heterogeneous datasets representing different hospital types—government, private, and multi-specialty centers—demonstrate that the proposed federated model achieves predictive performance comparable to centralized learning while offering significantly higher privacy guarantees [5][12][21]. The system proves effective for key clinical tasks including disease prediction, patient risk stratification, and length-of-stay forecasting [15][17][25]. This research provides a scalable, ethically compliant, and regulatory-aligned paradigm for privacy-preserving AI adoption in Indian healthcare, supporting collaborative analytics without compromising patient confidentiality [14][23].