App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 6

Federated semi-supervised learning via fuzzy reasoning and entropy constraints

Shi Tingboa,b
Gong Wenjuana,b
Li Chunhana,b
a. Qingdao Institute of Software, College of Computer Science and Technology, b. Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, China University of Petroleum (East China), Qingdao 266580, China
DOI: 10.19734/j.issn.1001-3695.2025.09.0412

Abstract

This study tackles model bias in Federal Semi-supervised Learning (FSSL) caused by scarce labeled data and heterogeneous data distributions. The research proposed a new learning scheme that uses fuzzy inference and entropy constraints. Most existing methods generate pseudo-labels from predictions to leverage unlabeled data. They usually set confidence thresholds to screen these predictions. However, these approaches often miss the deeper reasons behind low confidence scores. Unlabeled clients can show significant differences in their data distributions. Using noisy pseudo-labels without distinction degrades model performance. To resolve this, the team designed a fuzzy computation module to model uncertainty. This module explored potential information in low-confidence pseudo-labels, paying special attention to pseudo-labels from confusing categories. The scheme also introduced a filtering mechanism to use information entropy. This mechanism effectively removed low-quality unlabeled data and reduced the impact of noisy samples. The research created FedFREC, a cascaded learning algorithm for FSSL. The algorithm combines the fuzzy computation module for confusing categories with the entropy-based filtering mechanism for low-confidence samples. This approach successfully utilizes the information embedded in unlabeled data. Extensive experiments on the SVHN, CIFAR-10, CIFAR-100, fashion MNIST, and ISIC2018 datasets demonstrate the superiority of the proposed method.

Foundation Support

山东省自然科学基金资助项目(ZR2023MF041)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.09.0412
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 6

Publish History

[2026-02-25] Accepted Paper

Cite This Article

施庭波, 宫文娟, 李淳涵. 基于模糊推理和熵约束的联邦半监督学习算法 [J]. 计算机应用研究, 2026, 43 (6). (2026-02-25). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0412. (Shi Tingbo, Gong Wenjuan, Li Chunhan. Federated semi-supervised learning via fuzzy reasoning and entropy constraints [J]. Application Research of Computers, 2026, 43 (6). (2026-02-25). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0412. )

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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