Task-Load-Aware Game-Theoretic Framework for Wireless Federated Learning-Reference-Cited by-同舟云学术

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Task-Load-Aware Game-Theoretic Framework for Wireless Federated Learning

Published:2023-01 Issue:1 Volume:27 Page:268-272
ISSN:1089-7798
Container-title:IEEE Communications Letters
language:
Short-container-title:IEEE Commun. Lett.

Author:

Liu Jiawei¹^ORCID,Zhang Guopeng¹^ORCID,Wang Kezhi²^ORCID,Yang Kun³^ORCID

Affiliation:

1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China

2. Department of Computer and Information Science, Northumbria University, Newcastle, U.K.

3. Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Quzhou, China

Funder

National Natural Science Foundation of China

Quzhou Government

Sichuan Major R&D Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Modeling and Simulation

Link

http://xplorestaging.ieee.org/ielx7/4234/10008392/09905580.pdf?arnumber=9905580

Reference16 articles.

1. Distributed optimization with arbitrary local solvers

2. Resource Demand Prediction of Cloud Workloads Using an Attention-based GRU Model

3. Adaptive Bitrate Streaming in Wireless Networks With Transcoding at Network Edge Using Deep Reinforcement Learning

4. An Incentive Mechanism for Federated Learning in Wireless Cellular Networks: An Auction Approach

Cited by 3 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. Price-Discrimination Game for Distributed Resource Management in Federated Learning;IEEE Networking Letters;2024-06

2. Federated Learning Approach to Safeguard User Privacy;Advances in Healthcare Information Systems and Administration;2024-04-19

3. A game incentive mechanism for energy efficient federated learning in computing power networks;Digital Communications and Networks;2023-10

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