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Algorithm Research & Explore
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3281-3287

Knowledge distillation in federated learning based on latent space generator

Wang Hua
Wang Xiaofenga,b
Li Kea
a. School of Computer Science & Engineering, b. The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China

Abstract

User heterogeneity poses significant challenges to federated learning(FL), leading to global model bias and slow convergence. To address this problem, this paper proposed a method combining knowledge distillation and a latent space generator, called FedLSG. This method employed a central server to learn a generative model with a latent space generator that extracted and simulated the probability distribution of sample labels from different user devices, then generated richer and more diverse pseudo-samples to guide the training of user models. This approach aimed to effectively address the problem of user heterogeneity in FL. Theoretical analysis and experimental results show that FedLSG generally achieves about 1% higher test accuracy than the existing FedGen method, improves communication efficiency in the first 20 rounds, and provides a degree of user privacy protection.

Foundation Support

国家自然科学基金资助项目(62062001)
宁夏青年拔尖人才项目(2021)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.03.0084
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 11
Section: Algorithm Research & Explore
Pages: 3281-3287
Serial Number: 1001-3695(2024)11-011-3281-07

Publish History

[2024-07-31] Accepted Paper
[2024-11-05] Printed Article

Cite This Article

王虎, 王晓峰, 李可. 基于潜在空间生成器的联邦知识蒸馏 [J]. 计算机应用研究, 2024, 41 (11): 3281-3287. (Wang Hu, Wang Xiaofeng, Li Ke. Knowledge distillation in federated learning based on latent space generator [J]. Application Research of Computers, 2024, 41 (11): 3281-3287. )

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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