Energy-efficient distributed federated learning offloading and scheduling healthcare system in blockchain based networks?
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10252839" target="_blank" >RIV/61989100:27240/23:10252839 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2542660523001385" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2542660523001385</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.iot.2023.100815" target="_blank" >10.1016/j.iot.2023.100815</a>
Alternative languages
Result language
angličtina
Original language name
Energy-efficient distributed federated learning offloading and scheduling healthcare system in blockchain based networks?
Original language description
Many disease detection and prevention applications in digital healthcare systems are widely used but often focus only on prediction and classification, ignoring processing performance and data privacy issues. The study investigates the Energy-Efficient Distributed Federated Learning Offloading and Scheduling Healthcare Systems in Blockchain-Based Networks problem for healthcare applications. In order to solve the problem, the study presents the Energy-Efficient Distributed Federated Learning Offloading and Scheduling (EDFOS) system in blockchain based networks. EDFOS consisted of different schemes such as energy efficient offloading and scheduling and meet the quality of services (QoS) of applications during performing in the system. Simulation results show that EDFOS reduces power consumption by 39%, training and testing time by 29%, and resource leakage and deadlines by 36% compared to existing healthcare systems. The EDFOS platform is an effective solution for addressing the issues of power consumption and data privacy in healthcare applications.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - 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
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Internet of Things
ISSN
2543-1536
e-ISSN
2542-6605
Volume of the periodical
22
Issue of the periodical within the volume
2023
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
1-13
UT code for WoS article
001056598900001
EID of the result in the Scopus database
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