CrypTen-FL: A Secure Federated Learning Framework for Multi-Disease Prediction from MIMIC-IV Using Encrypted EHRs

Abstract: The increasing demand for privacy-preserving machine learning in healthcare has driven the need for federated approaches that ensure data confidentiality across institutions. In this work, we present CrypTen-FL, a secure federated learning framework for disease prediction using the MIMIC-IV electronic health record (EHR) dataset. CrypTen-FL enables collaborative model training across multiple hospitals without sharing raw patient data, thereby addressing critical privacy concerns through the integration of Secure Multi-Party Computation (SMPC) using CrypTen and differential privacy mechanisms. We adopt a Transformer-based neural architecture to effectively capture the temporal and high-dimensional nature of EHR data, enabling accurate prediction of multiple clinically significant conditions. The framework incorporates decentralized key generation, secure aggregation, and cross-institutional evaluation to assess generalization performance and robustness. Experimental results demonstrate that CrypTen-FL achieves competitive predictive performance while offering strong privacy guarantees, paving the way for secure and scalable AI applications in real-world healthcare settings.

Himanshu and Pushpendra Singh. “CrypTen-FL: A Secure Federated Learning Framework for Multi-Disease Prediction from MIMIC-IV Using Encrypted EHRs”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.9 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160922

@article{2025,
title = {CrypTen-FL: A Secure Federated Learning Framework for Multi-Disease Prediction from MIMIC-IV Using Encrypted EHRs},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160922},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160922},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {9},
author = {Himanshu and Pushpendra Singh}
}