A Hybrid Task Offloading and Resource Allocation Approach for Digital Twin-Empowered UAV-Assisted MEC Network Using Federated Reinforcement Learning for Future Wireless Network-Reference-Cited by-同舟云学术

A Hybrid Task Offloading and Resource Allocation Approach for Digital Twin-Empowered UAV-Assisted MEC Network Using Federated Reinforcement Learning for Future Wireless Network

Published:2024-02 Issue:1 Volume:70 Page:3120-3130
ISSN:0098-3063
Container-title:IEEE Transactions on Consumer Electronics
language:
Short-container-title:IEEE Trans. Consumer Electron.

Author:

Consul Prakhar¹^ORCID,Budhiraja Ishan¹^ORCID,Garg Deepak²^ORCID,Kumar Neeraj³^ORCID,Singh Ramendra⁴,Almogren Ahmad S.⁵^ORCID

Affiliation:

1. School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India

2. School of Computer Science and Artificial Intelligence, SR University, Warangal, India

3. Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India

4. Computer Science and Engineering, Raj Kumar Goel Institute of Technology, Ghaziabad, India

5. Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

Funder

King Saud University, Riyadh, Saudi Arabia, through Researchers Supporting Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/30/10510045/10443270.pdf?arnumber=10443270

Reference43 articles.

1. ChainFL: A Simulation Platform for Joint Federated Learning and Blockchain in Edge/Cloud Computing Environments

2. FLBCPS: Federated Learning based Secured Computation Offloading in Blockchain-Assisted Cyber-Physical Systems

3. When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multitimescale Resource Management for Multiaccess Edge Computing in 5G Ultradense Network

4. Federated Intrusion Detection in Blockchain-Based Smart Transportation Systems

5. A Hybrid Secure Resource Allocation and Trajectory Optimization Approach for Mobile Edge Computing Using Federated Learning Based on WEB 3.0

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