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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 3

Secure and efficient personalized federated learning framework for intelligent transportation

Wang Yifei1,2
Hu Ying1,2
Cai Ting1,2
Chen Wei1
Wu Yuxin3
Li Xiaoli3
Ye Zhiwei1,2
1. School of Computer Science, Hubei University of Technology, Wuhan 430068, China
2. Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network, Hubei University of Technology, Wuhan 430068, China
3. School of Computer Science, Hubei University of Arts and Science, Xiangyang 441053, China
DOI: 10.19734/j.issn.1001-3695.2025.08.0268

Abstract

Blockchain-federated learning (FL) integration enables decentralized and privacy-preserving collaboration in intelligent transportation systems, but device heterogeneity, non-IID data, and single-chain bottlenecks limit scalability and efficiency. This paper proposed a Secure and Cost-Efficient Personalized Federated Learning (SCPFL) framework. By leveraging blockchain sharding, SCPFL supports parallel training and aggregation across regions, while a cosine-similarity-based personalization method balances individual adaptation and cross-shard knowledge sharing. A dual mechanism of filtering and incentives further enhances robustness and participation. Experiments on CIFAR-10, MNIST, and German Traffic Sign Recognition Benchmark (GTSRB) datasets show that SCPFL improves accuracy by up to 18.3% and accelerates convergence by 50% compared with the state-of-the-art personalized baselines (e. g. , Ditto) . In terms of system efficiency, SCPFL achieves 53% higher throughput and reduces processing time, CPU, and memory usage by 33.3%, 17.7%, and 19.7% over traditional Main-Subchain FL architectures.

Foundation Support

国家自然科学基金资助项目(62302154,U23A20318,62306108,62376089)
湖北省自然科学基金资助项目(2024AFB882)
湖工大博士科研启动基金资助项目(XJ2022006701)
智能感知系统与安全教育部重点实验室开放基金资助项目(KLISSS202404)
湖北省教育厅科研重点项目(D20242602)
湖北省高等学校优秀中青年科技创新团队计划项目(T2023007)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.08.0268
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 3

Publish History

[2025-11-18] Accepted Paper

Cite This Article

王逸飞, 胡颍, 蔡婷, 等. 面向智能交通的安全高效个性化联邦学习框架 [J]. 计算机应用研究, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0268. (Wang Yifei, Hu Ying, Cai Ting, et al. Secure and efficient personalized federated learning framework for intelligent transportation [J]. Application Research of Computers, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0268. )

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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