Multi-Agent Deep Reinforcement Learning Based Incentive Mechanism for Multi-Task Federated Edge Learning-Reference-Cited by-同舟云学术

Multi-Agent Deep Reinforcement Learning Based Incentive Mechanism for Multi-Task Federated Edge Learning

Published:2023-10 Issue:10 Volume:72 Page:13530-13535
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Zhao Nan¹^ORCID,Pei Yiyang²^ORCID,Liang Ying-Chang³^ORCID,Niyato Dusit⁴^ORCID

Affiliation:

1. Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, China

2. Singapore Institute of Technology, Singapore

3. Center for Intelligent Networking and Communications (CINC), University of Electronic Science and Technology of China (UESTC), Chengdu, China

4. Nanyang Technological University, Singapore

Funder

Key Research and Development Plan of Hubei Province, China

Knowledge Innovation Special Project of Wuhan Science and Technology Bureau, China

National Key R&D Program of China

Key Areas of Research and Development Program of Guangdong Province, China

Fundamental Research Funds for the Central Universities

Programme of Introducing Talents of Discipline to Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/25/10287112/10129850.pdf?arnumber=10129850

Reference17 articles.

1. A Learning-Based Incentive Mechanism for Federated Learning

2. A Novel Joint Dataset and Computation Management Scheme for Energy-Efficient Federated Learning in Mobile Edge Computing

3. Energy-Efficient Multiprocessor-Based Computation and Communication Resource Allocation in Two-Tier Federated Learning Networks

4. Space/Aerial-Assisted Computing Offloading for IoT Applications: A Learning-Based Approach

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