Special Topics in Intelligent Transportation
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 5
1328-1337

Customized federated learning model framework and algorithm enhancements for vehicular networks

Li Hanqi1
Wang Xiaoni1
Wu Qiuxin1
Wang Can1
Wu Lang1
Du Junlong1
Qin Yu2
1. School of Applied Science, Beijing Information Science & Technology University, Beijing 100192, China
2. Trusted Computing & Information Assurance Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
DOI: 10.19734/j.issn.1001-3695.2023.09.0416

Abstract

This paper proposed an innovative customized service framework for the federated learning model of the Internet of Vehicles(IoV), which addressed the difficulty of meeting the needs of users to train personalized models in IoV federated learning services. This framework adopted a federated learning aggregation algorithm that integrated device contribution and dataset similarity, achieving personalized federated learning. This algorithm used different weight allocation methods and similarity calculations to enable different users to choose appropriate model training schemes based on their own needs and data characteristics. The framework also proposed a dual sampling validation method to address model performance and credibility issues, and utilized smart contracts to support data collaboration, ensuring data security. The experimental results show that the proposed algorithm exhibits high accuracy in most experimental scenarios, and this framework can significantly improve the personalized level of IoV services while ensuring the accuracy and reliability of the model.

Foundation Support

国家自然科学基金资助项目(61872343)
未来区块链与隐私计算高精尖中心资助项目(202203)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.09.0416
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 5
Section: Special Topics in Intelligent Transportation
Pages: 1328-1337
Serial Number: 1001-3695(2024)05-007-1328-10

Publish History

[2023-11-29] Accepted Paper
[2024-05-05] Printed Article

Cite This Article

李翰奇, 王小妮, 吴秋新, 等. 面向车联网的联邦学习模型定制框架及算法改进 [J]. 计算机应用研究, 2024, 41 (5): 1328-1337. (Li Hanqi, Wang Xiaoni, Wu Qiuxin, et al. Customized federated learning model framework and algorithm enhancements for vehicular networks [J]. Application Research of Computers, 2024, 41 (5): 1328-1337. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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