Federated-Learning Based Privacy Preservation and Fraud-Enabled Blockchain IoMT System for Healthcare
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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2023-02-01
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Language
en
Pages
9
664-672
664-672
Series
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Volume 27, issue 2
Abstract
These days, the usage of machine-learning-enabled dynamic Internet of Medical Things (IoMT) systems with multiple technologies for digital healthcare applications has been growing progressively in practice. Machine learning plays a vital role in the IoMT system to balance the load between delay and energy. However, the traditional learning models fraud on the data in the distributed IoMT system for healthcare applications are still a critical research problem in practice. The study devises a federated learning-based blockchain-enabled task scheduling (FL-BETS) framework with different dynamic heuristics. The study considers the different healthcare applications that have both hard constraint (e.g., deadline) and resource energy consumption (e.g., soft constraint) during execution on the distributed fog and cloud nodes. The goal of FL-BETS is to identify and ensure the privacy preservation and fraud of data at various levels, such as local fog nodes and remote clouds, with minimum energy consumption and delay, and to satisfy the deadlines of healthcare workloads. The study introduces the mathematical model. In the performance evaluation, FLBETS outperforms all existing machine learning and blockchain mechanisms in fraud analysis, data validation, energy and delay constraints for healthcare applications.Description
Publisher Copyright: IEEE
Keywords
Blockchain, Federated Learning, Fraud-Analysis, Healthcare, Internet of Things, IoMT, Medical services, Privacy Preservation, Security
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Citation
Lakhan, A, Mohammed, M A, Nedoma, J, Martinek, R, Tiwari, P, Vidyarthi, A, Alkhayyat, A & Wang, W 2023, ' Federated-Learning Based Privacy Preservation and Fraud-Enabled Blockchain IoMT System for Healthcare ', IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 2, pp. 664-672 . https://doi.org/10.1109/JBHI.2022.3165945
