App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 4

History-aware dynamic federated learning method for data heterogeneous environments

Luo Haowena,b,c
Sheng Zhiweia,b,c
a. School of Cybersecurity (Xin Gu Industrial College), b. Advanced Cryptography System Security Key Laboratory of Sichuan Province, c. SUGON Industrial Control and Security Center, Chengdu University of Information Technology, Chengdu 610225, China
DOI: 10.19734/j.issn.1001-3695.2025.07.0286

Abstract

Federated learning enables global machine learning model training while preserving local data privacy. However, it suffers from low model accuracy and slow convergence speed due to data heterogeneity. To address these issues, this paper proposed a dynamic federated learning method named FedHD (federated learning with historical data) . FedHD dynamically corrected the update direction of client models based on historical local models and global model information. This approach improved the global model’s accuracy while ensuring smoothness and robustness during the convergence process. In addition, clustering based on model similarity optimized client sampling. A sliding window mechanism incorporated historical model data, enhancing the usability and robustness of the sampling process and accelerating global model convergence. The study evaluated the method under various heterogeneous data scenarios. The results showed that FedHD outperformed other algorithms in terms of model accuracy and convergence speed on MNIST and CIFAR-10, while demonstrating better robustness. These findings indicate that the proposed method performs well in heterogeneous data environments.

Foundation Support

成都市重点研发支撑计划项目(2024-YF05-01227-SN)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.07.0286
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 4

Publish History

[2025-12-12] Accepted Paper

Cite This Article

罗浩文, 盛志伟. 异构环境下基于历史模型的动态联邦学习方法 [J]. 计算机应用研究, 2026, 43 (4). (2025-12-19). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0286. (Luo Haowen, Sheng Zhiwei. History-aware dynamic federated learning method for data heterogeneous environments [J]. Application Research of Computers, 2026, 43 (4). (2025-12-19). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0286. )

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.

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