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Algorithm Research & Explore
|
1102-1107

Attention-driven feature separation method for personalized federated learning

Zhang Xiaoqin1,2
Jin Xixing1
Lu Yanjun1
Cao Zeyu1
1. School of Computer Science & Engineering, Chongqing University of Technology, Chongqing 400054, China
2. Chongqing Communication Design Institute Co. , Ltd. , Chongqing 400041, China

Abstract

This paper proposed an FedAM to address the challenges of poor model convergence and the lack of personalized solutions in highly heterogeneous data environments faced by traditional federated learning. FedAM achieved adaptive, dyna-mic separation of global and personalized features by decomposing the model into a feature extraction layer and a model head, with an added attention module to extract global and personalized information separately. Additionally, FedAM incorporated correlation alignment loss to balance personalization and generalization capabilities. Experimental results demonstrate that FedAM exhibits outstanding performance, maintaining robust results even with frequent client dropouts, and flexibly adapting to heterogeneous data environments, thereby significantly enhancing both personalization and generalization. FedAM effectively improves the overall performance and adaptability of federated learning models, providing strong support for complex federated learning scenarios.

Foundation Support

重庆市技术创新与应用发展专项重点资助项目(CSTB2022TIAD-KPX0054)
重庆理工大学研究生教育高质量发展项目(gzlcx20243154)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.09.0325
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 4
Section: Algorithm Research & Explore
Pages: 1102-1107
Serial Number: 1001-3695(2025)04-018-1102-06

Publish History

[2025-04-05] Printed Article

Cite This Article

张晓琴, 金西兴, 陆艳军, 等. 注意力机制驱动的个性化联邦学习特征分离方法 [J]. 计算机应用研究, 2025, 42 (4): 1102-1107. (Zhang Xiaoqin, Jin Xixing, Lu Yanjun, et al. Attention-driven feature separation method for personalized federated learning [J]. Application Research of Computers, 2025, 42 (4): 1102-1107. )

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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