App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 11

Personalized federated learning method based on fuzzy clustering and meta-learning

Ma Xuan1,2,3
Ge Lina1,2,3
Wang Jie1,2,3,4
1. School of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
2. Key Laboratory of Intelligent Computing Network and Big Data Information Security, Guangxi Minzu University, Nanning 530006, China
3. Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Nanning 530006, China
4. School of Computer Science and Engineering, School of Software, School of Artificial Intelligence, Guangxi Normal University, Guilin 541004, China
DOI: 10.19734/j.issn.1001-3695.2026.02.0074

Abstract

Existing personalized federated learning methods mostly adopt a hard clustering mechanism, assuming that each client belongs to only a single cluster, which fails to capture the overlap in client data distributions in real-world scenarios. Furthermore, their optimization objectives typically focus on the consistency of cluster-level models rather than directly targeting personalized performance for individual clients. Therefore, this paper proposes a personalized federated learning framework named FC-FML. The method first quantifies the data distribution of each client and uses a fuzzy clustering algorithm to partition clients into overlapping clusters, generating a membership matrix between clients and clusters. Subsequently, through multiple iterations, the framework maintains and updates a set of cluster-level meta-models. Each client achieves personalization by fine-tuning the cluster-level meta-models using its local data. This strategy mitigates the negative impact of data heterogeneity while fully leveraging the ability of meta-models to quickly adapt to different data distributions, thereby achieving better personalized adaptation. To demonstrate the effectiveness of the proposed method, this paper evaluates the FC-FML framework on three benchmark datasets and against six baseline methods. Experimental results show that FC-FML outperforms other methods.

Foundation Support

国家自然科学基金(61862007)
广西自然科学基金(2024GXNSFAA010111)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.02.0074
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 11

Publish History

[2026-07-13] Accepted Paper

Cite This Article

马轩, 葛丽娜, 王捷. 基于模糊聚类和元学习的个性化联邦学习方法 [J]. 计算机应用研究, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.02.0074. (Ma Xuan, Ge Lina, Wang Jie. Personalized federated learning method based on fuzzy clustering and meta-learning [J]. Application Research of Computers, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.02.0074. )

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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