Federated Deep Reinforcement Learning for Task Offloading in Digital Twin Edge Networks-Reference-Cited by-同舟云学术

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Federated Deep Reinforcement Learning for Task Offloading in Digital Twin Edge Networks

Published:2024-05 Issue:3 Volume:11 Page:2849-2863
ISSN:2327-4697
Container-title:IEEE Transactions on Network Science and Engineering
language:
Short-container-title:IEEE Trans. Netw. Sci. Eng.

Author:

Dai Yueyue¹^ORCID,Zhao Jintang¹,Zhang Jing²,Zhang Yan³^ORCID,Jiang Tao¹^ORCID

Affiliation:

1. Research Center of 6G Mobile Communications, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

2. Institute of Space Integrated Ground Network, Hefei, China

3. Department of Informatics, University of Oslo, Oslo, Norway

Funder

National Natural Science Foundation of China

State Key Laboratory of Rail Traffic Control and Safety

Beijing Jiaotong University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488902/10510356/10384610.pdf?arnumber=10384610

Reference36 articles.

1. 6G white paper on edge intelligence;Peltonen,2020

2. Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models

3. Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks

4. Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions

5. Digital Twin-Driven Vehicular Task Offloading and IRS Configuration in the Internet of Vehicles

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2. Clustering and power allocation for secured access to simultaneous transmissions and sensing Internet of Things networks;Wireless Networks;2024-05-22

3. Energy-Efficient UAV Scheduling and Probabilistic Task Offloading for Digital Twin-Empowered Consumer Electronics Industry;IEEE Transactions on Consumer Electronics;2024-02

4. FeDRL-D2D: Federated Deep Reinforcement Learning- Empowered Resource Allocation Scheme for Energy Efficiency Maximization in D2D-Assisted 6G Networks;IEEE Access;2024

5. Sonar Digital Twin Layer via Multiattention Networks With Feature Transfer;IEEE Transactions on Geoscience and Remote Sensing;2024

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