Adversarial Robust Federated Learning for Secure Mortality Risk Prediction using Multi-Institutional Electronic Health Records (EHRs)

Summary

This study proposes an Adversarial-Robust Federated Learning (AR-FL) model for mortality risk prediction using multi-institutional Electronic Health Records. The model integrates min–max adversarial training, domain-aware attention, and privacy-preserving aggregation to enhance robustness, adaptability, and data confidentiality, enabling a secure, reliable, and generalizable clinical decision-support system suitable for heterogeneous healthcare environments.

Abstract

Electronic Health Records (EHRs) are the main source of data that enable data-driven clinical decision-making. However, the sensitive character of EHRs and the strict privacy policies in the healthcare sector make centralized model training difficult or impossible. An Adversarially Robust Federated Learning (AR-FL) model is proposed to predict patient mortality risk across different institutions without sharing the original EHR data. The primary aim of this study is to introduce a secure, reproducible, and scalable procedure for training privacy-preserving, adversarially resilient predictive models across diverse clinical settings. This study employs a min–max adversarial training approach at each institution to improve robustness against worst-case perturbations. A domain-aware attention mechanism is also employed to dynamically adapt to differences in clinical feature distributions within the institution. For confidentiality, the model updates are pooled using privacy-protecting methods that block the revealing of sensitive patient data during federated communication. This study specifies the entire process from data preprocessing and adversarial example generation to local training, secure aggregation, and global model evaluation, allowing for a consistent implementation across various healthcare environments. Experimental validations demonstrate that the AR-FL model achieves superior predictive performance, adversarial robustness, and cross-institutional generalization. By establishing a standardized training and evaluation pipeline, this study supports the development of reliable and ethically compliant clinical decision-support systems.

Introduction

Federated Learning (FL) has emerged as a transformative paradigm for privacy-preserving healthcare analytics by enabling collaborative model training across decentralized electronic health record (EHR) data without sharing raw patient information. This approach mitigates data silos and regulatory constraints while improving predictive performance in tasks such as mortality prediction. However, despite these advantages, FL systems remain vulnerable to adversarial and poisoning attacks. Figure 1 illustrates the collaborative architecture and workflow of federated learning across distributed clinical institutions.

Protocol

Proposed methodology
The proposed Adversarially Robust Federated Learning (AR-FL) model aims to predict patient mortality risk securely and collaboratively across different healthcare institutions, while protecting data privacy and making the system resilient to adversarial perturbations. The deployment includes four primary elements: (1) the Federated Learning structure, (2) the min-max optimization-based adversarial training, (3) the domain-aware attention mechanism, and (4) the privacy-preserving parameter aggregation. The methodology is executed in a systematic flow designated for adversarial robust federated learning to estimate the mortality....

Results

The AR-FL model was tested on two publicly available, de-identified, multi-institutional electronic health record (EHR) datasets: the MIMIC-III and the eICU Collaborative Research Database, which include a variety of ICU patient records with in-hospital mortality outcomes. The dataset was spread across simulated hospital scenarios representing the actual differences among institutions regarding their data characteristics. A separate model was trained for each client (institution) using its own private EHR data, and the a.......

Discussion

This paper presents an Adversarial-Robust Federated Learning (AR-FL) model aimed at enhancing mortality risk prediction using multi-institutional Electronic Health Records (EHRs) by improving accuracy, robustness, and generalizability. The study reveals that incorporating adversarial training into the federated environment will significantly benefit the model by making it more resistant to input perturbations and domain variability across different institutions. These new results are in line with previous ones, which sho.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Adam Optimizer	PyTorch	https://pytorch.org/docs/stable/generated/torch.optim.Adam.html	Optimization
CUDA Toolkit	NVIDIA	https://developer.nvidia.com/cuda-toolkit	Version 11.8; GPU acceleration
Differential Privacy Library	Opacus	https://opacus.ai	Version 1.3.0; DP-based privacy-preserving training
eICU Collaborative Research Database	PhysioNet	https://physionet.org/content/eicu-crd/2.0/	ICU patient EHR data
MIMIC-III	PhysioNet	https://physionet.org/content/mimiciii/1.4/	ICU patient EHR data
PGD Attack Script	GitHub	https://github.com/Saswati-C/AR-FL-HER	Version: commit-hash; Adversarial example generation
PyTorch	PyTorch.org	https://pytorch.org	Version 2.0; Model implementation and training
Tesla V100 GPU	NVIDIA	https://www.nvidia.com/en-us/data-center/v100/	Model training