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

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.

@article{Himanshu2025,
  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},
  volume    = {16},
  number    = {9},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Himanshu and Pushpendra Singh},
  doi       = {10.14569/IJACSA.2025.0160922},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160922}
}