An Optimal Transport-Based Federated Reinforcement Learning Approach for Resource Allocation in Cloud–Edge Collaborative IoT-Reference-Cited by-同舟云学术

An Optimal Transport-Based Federated Reinforcement Learning Approach for Resource Allocation in Cloud–Edge Collaborative IoT

Published:2024-01-15 Issue:2 Volume:11 Page:2407-2419
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Gan Deqiao¹^ORCID,Ge Xiaohu²^ORCID,Li Qiang²^ORCID

Affiliation:

1. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China

2. School of Electronic Information and Communications and the China International Joint Research Center of Green Communications and Networking, Huazhong University of Science and Technology, Wuhan, China

Funder

National Natural Science Foundation of China

Research on Theories of Intelligent Network Cognition Based on Multi-access Edge Computing

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10384482/10173668.pdf?arnumber=10173668

Reference46 articles.

1. Live Data Analytics With Collaborative Edge and Cloud Processing in Wireless IoT Networks

2. Federated Learning for 6G: Applications, Challenges, and Opportunities

3. Multi-key privacy-preserving deep learning in cloud computing

4. Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

5. Federated Learning in Mobile Edge Networks: A Comprehensive Survey

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