Contract Theory Based Incentive Mechanism for Clustered Vehicular Federated Learning-Reference-Cited by-同舟云学术

Contract Theory Based Incentive Mechanism for Clustered Vehicular Federated Learning

Published:2024-07 Issue:7 Volume:25 Page:8134-8147
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Wang Siyang¹^ORCID,Zhao Haitao¹^ORCID,Wen Wanli²^ORCID,Xia Wenchao¹^ORCID,Wang Bin³^ORCID,Zhu Hongbo¹^ORCID

Affiliation:

1. Jiangsu Key Laboratory of Wireless Communications, and the Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing, China

2. School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China

3. College of Electrical Engineering, Zhejiang University, Hangzhou, China

Funder

National Natural Science Foundation of China

Jiangsu Natural Science Foundation for Distinguished Young Scholars

Jiangsu Provincial Key Research and Development Program

Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu

China Postdoctoral Science Foundation

Natural Science Foundation of Chongqing, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6979/10582787/10480913.pdf?arnumber=10480913

Reference37 articles.

1. FedVANET: Efficient Federated Learning with Non-IID Data for Vehicular Ad Hoc Networks

2. FEEL: Federated End-to-End Learning With Non-IID Data for Vehicular Ad Hoc Networks

3. Communication-efficient learning of deep networks from decentralized data;McMahan

4. Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT

5. Adaptive Hierarchical Federated Learning over Wireless Networks

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