Sustainability and Latency Reduction Through Federated Learning-Powered Digital Twins in IoT Devices-Reference-Cited by-同舟云学术

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Sustainability and Latency Reduction Through Federated Learning-Powered Digital Twins in IoT Devices

Published:2024-03-14 Issue: Volume: Page:211-217
ISSN:
Container-title:2024 16th International Conference on Computer and Automation Engineering (ICCAE)
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Author:

Abdullah Qazwan¹,Salh Adeb²,Ahmed Mustafa Sami³,Mohd Shah Nor Shahida¹,Aydoğdu Ömer⁴,Hussain Ghasan Ali⁵

Affiliation:

1. Universiti Tun Hussein Onn Malaysia,Faculty of Engineering Technology,Johor,Malaysia

2. University Tunku Abdul Rahman (UTAR),Faculty of Information and Communication Technology,Perak,Malaysia

3. University of Technology,Department of Communication Engineering,Iraq

4. Konya Technical University,Faculty of Engineering & Natural Sciences,Konya,Turkey,42250

5. University of Kufa,Faculty of Engineering,Department of Electrical Engineering,Iraq

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10569140/10569143/10569209.pdf?arnumber=10569209

Reference26 articles.

1. Optimizing Federated Learning With Deep Reinforcement Learning for Digital Twin Empowered Industrial IoT

2. A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

3. Digital Twin Networks: A Survey

4. Experimentable Digital Twins—Streamlining Simulation-Based Systems Engineering for Industry 4.0

5. Edge Intelligence and Blockchain Empowered 5G Beyond for the Industrial Internet of Things

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