In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 1
125-132

Similarity-based personalized federated learning model aggregation framework

Wu Wenxuan1
Wang Can1
Huang Jingjing1
Wu Qiuxin1
Qin Yu2
1. School of Applied Science, Beijing Information Science & Technology University, Beijing 100192, China
2. Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
DOI: 10.19734/j.issn.1001-3695.2024.06.0205

Abstract

In traditional federated learning, global model obtained through weighted aggregation cannot address the issue of cross-client data heterogeneity. Existing research addresses the problem by forming personalized models, but balancing the global common information and local personality information remains a challenge. In response to the above problems, this paper proposed FedPG, a personalized federated learning model aggregation framework. Based on the similarity of the client models, FedPG used the cosine similarity of the normalized model parameter changes as the personalized weight of model aggregation, thereby realizing personalized client-oriented global model aggregation. By introducing a smoothing coefficient, this framework could flexibly adjust the proportion of common and personalized information in the model. To reduce the cost of selecting the smoothing coefficient, this paper further proposed the FedPGS framework, which scheduled the smoothing coefficient. In the experiments, the FedPG and FedPGS frameworks improve the accuracy of the FedAvg, FedProto, and FedProx algorithms on datasets with feature distribution shift by an average of 1.20 to 11.50 percentage points, and reduce the impact of malicious devices on model accuracy. The results indicate that the FedPG and FedPGS frameworks can effectively enhance model accuracy and robustness in scenarios with data heterogeneity and malicious device interference.

Foundation Support

国家自然科学基金资助项目(61604014)
未来区块链与隐私计算高精尖项目(GJJ-23)
北京信息科技大学“青年骨干教师”支持计划项目(YBT202450)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.06.0205
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 1
Section: Algorithm Research & Explore
Pages: 125-132
Serial Number: 1001-3695(2025)01-018-0125-08

Publish History

[2025-01-05] Printed Article

Cite This Article

武文媗, 王灿, 黄静静, 等. 基于相似性的个性化联邦学习模型聚合框架 [J]. 计算机应用研究, 2025, 42 (1): 125-132. (Wu Wenxuan, Wang Can, Huang Jingjing, et al. Similarity-based personalized federated learning model aggregation framework [J]. Application Research of Computers, 2025, 42 (1): 125-132. )

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)