Open Access

ARTICLE

Secure Malicious Node Detection in Decentralized Healthcare Networks Using Cloud and Edge Computing with Blockchain-Enabled Federated Learning

Raj Sonani¹, Reham Alhejaili^2,*, Pushpalika Chatterjee³, Khalid Hamad Alnafisah⁴, Jehad Ali^5,*

1 Cornell University, Ithaca, NY 14850, USA
2 Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, 23218, Saudi Arabia
3 The Huntington National Bank, Columbus, OH 43074, USA
4 Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Arar, 91431, Saudi Arabia
5 Department of AI Convergence Network, Ajou University, Suwon, 16499, Republic of Korea

* Corresponding Authors: Reham Alhejaili. Email: ; Jehad Ali. Email:

Computer Modeling in Engineering & Sciences 2025, 144(3), 3169-3189. https://doi.org/10.32604/cmes.2025.070225

Received 10 July 2025; Accepted 05 September 2025; Issue published 30 September 2025

Abstract

Healthcare networks are transitioning from manual records to electronic health records, but this shift introduces vulnerabilities such as secure communication issues, privacy concerns, and the presence of malicious nodes. Existing machine and deep learning-based anomalies detection methods often rely on centralized training, leading to reduced accuracy and potential privacy breaches. Therefore, this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection (BFL-MND) model. It trains models locally within healthcare clusters, sharing only model updates instead of patient data, preserving privacy and improving accuracy. Cloud and edge computing enhance the model’s scalability, while blockchain ensures secure, tamper-proof access to health data. Using the PhysioNet dataset, the proposed model achieves an accuracy of 0.95, F1 score of 0.93, precision of 0.94, and recall of 0.96, outperforming baseline models like random forest (0.88), adaptive boosting (0.90), logistic regression (0.86), perceptron (0.83), and deep neural networks (0.92).

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Keywords

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