Federated-Learning Based Privacy Preservation and Fraud-Enabled Blockchain IoMT System for Healthcare

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10251456" target="_blank" >RIV/61989100:27240/23:10251456 - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/record/display.uri?eid=2-s2.0-85128336608&origin=resultslist&sort=plf-f" target="_blank" >https://www.scopus.com/record/display.uri?eid=2-s2.0-85128336608&origin=resultslist&sort=plf-f</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/JBHI.2022.3165945" target="_blank" >10.1109/JBHI.2022.3165945</a>

Result language

angličtina

Original language name

Federated-Learning Based Privacy Preservation and Fraud-Enabled Blockchain IoMT System for Healthcare

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Journal of Biomedical and Health Informatics

ISSN

2168-2194

e-ISSN

2168-2208

Volume of the periodical

27

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

9

Pages from-to

664-672

UT code for WoS article

000943693600012

EID of the result in the Scopus database

2-s2.0-85128336608