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

K-nearest neighbor adaptive personalized federated learning for civil aviation heterogeneous data

Wu Zeqin
Pei Xikai
Wang Ruijin
Zhang Fengli
Cyberspace Security Laboratory, School of Information & Software Engineering, University of Electronic Science & Technology of China, Chengdu 610054, China
DOI: 10.19734/j.issn.1001-3695.2025.08.0423

Abstract

This paper addresses the poor personalization performance of federated learning under heterogeneous civil aviation data by proposing FedLFL, a personalized federated learning framework based on a local feature library. During global training, FedLFL employs FedAvg to aggregate deep neural network parameters. After convergence, each client optimizes the intermediate features extracted by the global model via an information bottleneck module, constructs a local k-nearest neighbor classifier, and adaptively fuses global predictions with local similarity retrieval through dynamic interpolation. Experiments on four non-IID benchmarks—CIFAR-10, CIFAR-100, FEMNIST, and Shakespeare—show that FedLFL achieves average test accuracies of 90.1%, 60.1%, 90.2%, and 52.4%, respectively, significantly outperforming FedAvg, Ditto, and FedRep. The results demonstrate that FedLFL effectively enhances personalization and generalization while preserving data privacy.

Foundation Support

国家自然科学基金资助项目(62271128,U2333207)
四川省揭榜挂帅项目(2023YFG0374)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.08.0423
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 6

Publish History

[2026-02-27] Accepted Paper

Cite This Article

吴泽沁, 裴锡凯, 王瑞锦, 等. 面向民航异构数据的K近邻适应个性化联邦学习 [J]. 计算机应用研究, 2026, 43 (6). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0423. (Wu Zeqin, Pei Xikai, Wang Ruijin, et al. K-nearest neighbor adaptive personalized federated learning for civil aviation heterogeneous data [J]. Application Research of Computers, 2026, 43 (6). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0423. )

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.

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