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

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).