Digital Twin Enhanced Federated Reinforcement Learning With Lightweight Knowledge Distillation in Mobile Networks-Reference-Cited by-同舟云学术

Digital Twin Enhanced Federated Reinforcement Learning With Lightweight Knowledge Distillation in Mobile Networks

Published:2023-10 Issue:10 Volume:41 Page:3191-3211
ISSN:0733-8716
Container-title:IEEE Journal on Selected Areas in Communications
language:
Short-container-title:IEEE J. Select. Areas Commun.

Author:

Zhou Xiaokang¹^ORCID,Zheng Xuzhe²,Cui Xuesong³,Shi Jiashuai²,Liang Wei⁴^ORCID,Yan Zheng⁵^ORCID,Yang Laurence T.⁶^ORCID,Shimizu Shohei¹^ORCID,Wang Kevin I-Kai⁷^ORCID

Affiliation:

1. Faculty of Data Science, Shiga University, Hikone, Japan

2. School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha, China

3. School of Intelligent Engineering and Intelligent Manufacturing, Hunan University of Technology and Business, Changsha, China

4. Xiangjiang Laboratory, and the Changsha Social Laboratory of Artificial Intelligence, Hunan University of Technology and Business, Changsha, China

5. State Key Laboratory on Integrated Services Networks, and the School of Cyber Engineering, Xidian University, Xi’an, China

6. Department of Computer Science, St. Francis Xavier University, Antigonish, Canada

7. Department of Electrical, Computer and Software Engineering, The University of Auckland, Auckland, New Zealand

Funder

Grantsin-Aid for Scientific Research (C) from the Japan Society for the Promotion of Science

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/49/10297956/10269106.pdf?arnumber=10269106

Reference33 articles.

1. Adaptive Federated Deep Reinforcement Learning for Proactive Content Caching in Edge Computing

2. Federated Reinforcement Learning for Energy Management of Multiple Smart Homes With Distributed Energy Resources

3. SemCKD: Semantic calibration for cross-layer knowledge distillation;wang;IEEE Trans Knowl Data Eng,2023

4. Cybertwin-driven Federated Learning based Personalized Service Provision for 6G-V2X

5. Learning structured sparsity in deep neural networks;wei;Proc Adv Neural Inf Process Syst,2016

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. GRAPHITE — Generative Reasoning and Analysis for Predictive Handling in Traffic Efficiency;Information Fusion;2024-06

2. HCEC: An efficient geo-distributed deep learning training strategy based on wait-free back-propagation;Journal of Systems Architecture;2024-03

3. Federated distillation and blockchain empowered secure knowledge sharing for Internet of medical Things;Information Sciences;2024-03

4. Malicious traffic detection for cloud-edge-end networks: A deep learning approach;Computer Communications;2024-02

5. Semantic-Enhanced Knowledge Graph Completion;Mathematics;2024-01-31