App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 5

Survey on computational efficiency optimization methods for federated learning

Dong Hua1,2,3
Fan Jing1,2,3
Xi Enkang1,2,3
Jin Yadong1,2,3
Yu Hao1,2,3
Sun Yihang1,2,3
1. College of Electrical and Information Technology, Yunnan Minzu University, Kunming 650500, China
2. Yunnan Key Laboratory of Unmanned Autonomous System(Yunnan Minzu University), Kunming 650500, China
3. University Key Laboratory of Information and Communication on Security Backup and Recovery in Yunnan Minzu University, Kunming 650500, China
DOI: 10.19734/j.issn.1001-3695.2025.10.0397

Abstract

Federated learning, as a distributed machine learning paradigm, enables collaborative model training with privacy preservation through parameter aggregation mechanisms, demonstrating potential in addressing data silo issues in fields such as smart healthcare and the Internet of Things. However, computational efficiency bottlenecks in resource-constrained devices severely hinder its practical deployment. Compared to traditional centralized learning paradigms, federated learning still faces challenges such as high communication overhead and insufficient adaptability to dynamic environments while protecting data privacy. This paper systematically reviews efficiency enhancement methods for federated learning from three major directions: model compression, update strategy optimization, and data computation optimization. Existing studies show that collaborative optimization of compression strategies and dynamic resource allocation reduces communication overhead and accelerates convergence. Nevertheless, current approaches still encounter challenges including model accuracy loss and inadequate adaptability to dynamic environments. Finally, we propose future research directionsto explore lightweight native model architectures, intelligent dynamic update mechanisms, and efficient malicious detection methods, aiming to balance efficiency and security for promoting the large-scale application of federated learning in complex scenarios.

Foundation Support

国家自然科学基金资助项目(12361104)
教育部-新一代信息技术创新项目(2023IT077)
云南省教育厅科学研究基金资助项目(2025Y0670)
云南省吴中海专家工作站(202305AF150045)
云南省教育厅科学研究基金资助项目(2023Y0499)
CCF-深信服"远望"科研基金资助项目(20240210)
云南省教育厅科学研究基金资助项目(2025Y0670)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.10.0397
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 5

Publish History

[2026-01-20] Accepted Paper

Cite This Article

董华, 范菁, 郗恩康, 等. 联邦学习的计算效率优化方法综述 [J]. 计算机应用研究, 2026, 43 (5). (2026-01-20). https://doi.org/10.19734/j.issn.1001-3695.2025.10.0397. (Dong Hua, Fan Jing, Xi Enkang, et al. Survey on computational efficiency optimization methods for federated learning [J]. Application Research of Computers, 2026, 43 (5). (2026-01-20). https://doi.org/10.19734/j.issn.1001-3695.2025.10.0397. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)