Privacy-Preserving Federated Learning via Differential Privacy and Homomorphic Encryption for Cardiovascular Disease Risk Modeling

2026-04-30

AI Summary

This study addresses the tension between privacy preservation and modeling efficiency in multi-institutional healthcare collaboration by systematically evaluating a federated learning framework that integrates differential privacy (DP) and homomorphic encryption (HE) on real-world national cardiovascular disease data. It presents the first empirical comparison of DP and HE in both logistic regression and neural network models, revealing that neural networks exhibit greater robustness to DP noise, while HE achieves accuracy close to that of centralized models at the cost of substantial computational overhead. The findings offer a practical, deployable solution for fragmented healthcare systems, effectively balancing privacy guarantees, model utility, and implementation feasibility.

Abstract

Protecting sensitive health data while enabling collaborative analysis is a central challenge in healthcare. Traditional machine learning (ML) requires institutions to pool anonymized patient records, centralizing analytical development and privacy risks at a single site. Privacy-enhancing technologies (PETs), including Differential Privacy (DP) and Homomorphic Encryption (HE), can mitigate these risks. However, they are mainly studied in conventional data-sharing settings and often introduce trade-offs, including reduced model utility, higher computational cost, and increased implementation complexity. Federated Learning (FL) reduces data centralization by enabling institutions to train models locally and share only model updates. Nevertheless, FL does not eliminate privacy risks, as shared parameters or gradients may still reveal sensitive information. Integrating DP or HE into FL can strengthen privacy guarantees, yet their comparative performance and deployment implications in real-world healthcare settings remain unclear. We systematically evaluated DP and HE integration in FL under real-world conditions, comparing them with standard FL and centralized ML (cML) to quantify privacy-utility trade-offs in multi-institutional settings. Using nationwide Swedish healthcare data, we evaluated cardiovascular disease risk prediction using logistic regression (LR) and neural network (NN) learners. FL with HE achieved performance comparable to cML but introduced measurable cryptographic overhead, particularly in the NN implementation. FL with DP incurred lower computational cost; however, LR was more sensitive to calibrated noise than the NN, resulting in greater performance degradation. Our findings provide practical guidance for deploying privacy-preserving FL in fragmented healthcare systems.

Problem

• Federated Learning
• Differential Privacy
• Homomorphic Encryption
• Privacy-Preserving
• Cardiovascular Disease Risk Modeling

Innovation

• Federated Learning
• Differential Privacy
• Homomorphic Encryption
• Privacy-Preserving Machine Learning
• Cardiovascular Risk Prediction