ORIGINAL RESEARCH article
Front. Big Data
Sec. Cybersecurity and Privacy
Volume 8 - 2025 | doi: 10.3389/fdata.2025.1659026
EnDuSecFed: An Ensemble approach for Privacy Preserving Federated Learning with Dual-security Framework for sustainable Healthcare
Provisionally accepted
- Institute of Technology, Nirma University, Ahmedabad, India
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Recent advances in Artificial Intelligence have highlighted the role of Machine Learning in healthcare 3 decision-making, but centralized data collection raises significant privacy risks. Federated Learning 4 addresses this by enabling collaborative training across multiple clients without sharing raw data. However, 5 Federated Learning remains vulnerable to security threats that can compromise model reliability. This 6 paper proposes a dual-security Federated Learning framework that integrates Fernet Symmetric Encryption 7 for secure transmission of model updates using symmetric encryption and an Intrusion Detection System 8 to detect anomalous client behavior. Experiments on a publicly available healthcare dataset show 9 that the proposed system enhances privacy and robustness compared to traditional FL. Among tested 10 models, including Logistic Regression, Random Forest, and SVC, the ensemble method achieved the best 11 performance with 99% accuracy.
Keywords: Federated learning, Fernet Symmetric Encryption, Intrusion detection system, Logistic regression, random forest, Support vector classifier
Received: 03 Jul 2025; Accepted: 16 Oct 2025.
Copyright: © 2025 Shrimali. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Bela Shrimali, bela.shrimali@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.