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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 4
1113-1118

Linear coding-based federated learning

Shi Hongwei1a,1b,2
Wang Zhichao1a,1b
Shi Lianmin3,4
Yang Yingyao3
1. a. School of Information Engineering, b. Institute for Industrial Technology Research, Suqian University, Suqian Jiangsu 223800, China
2. School of Information & Control Engineering, China University of Mining & Technology, Xuzhou Jiangsu 221116, China
3. School of Computer Science & Technology, Soochow University, Suzhou Jiangsu 215031, China
4. The Key Laboratory of Cognitive Computing & Intelligent Information Processing of Fujian Education Institutions, Wuyi University, Wuyishan Fujian 354300, China
DOI: 10.19734/j.issn.1001-3695.2022.08.0449

Abstract

Federated learning can protect the data privacy of edge devices in collaborative training of edge devices. In the general FL scenarios, the participants of FL are usually composed of heterogeneous edge devices, where resource-constrained devices will consume more time, resulting in the decline of the training speed. The existing schemes either ignore stragglers, or distribute the computing tasks according to the distributed idea, but the distribution process involves the transmission of raw data, which cannot guarantee data privacy. To alleviate the straggler problem in small or medium-sized multiple heterogeneous devices scenario, this paper proposed a coding-based FL scheme, and it designed an efficient scheduling algorithm combined with the mathematical characteristics of linear coding to ensure data privacy and accelerate the speed of heterogeneous FL system. Meanwhile, the experimental results completed in the actual experimental platform show that when the performance difference between heterogeneous devices is large, the coding-based FL scheme can shorten the training time of the straggler by 92.85%.

Foundation Support

2021江苏省重点研发计划(现代农业)资助项目(BE2021354)
2020宿迁市项目(Z2020133)
2021宿迁市现代农业项目(L202109)
2019年苏州市科技计划资助项目(SNG201908)
认知计算与智能信息处理福建省高校重点实验室开放课题基金资助项目(KLCCIIP2021201)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.08.0449
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 4
Section: Algorithm Research & Explore
Pages: 1113-1118
Serial Number: 1001-3695(2023)04-025-1113-06

Publish History

[2022-11-14] Accepted Paper
[2023-04-05] Printed Article

Cite This Article

史洪玮, 王志超, 施连敏, 等. 线性编码联邦学习 [J]. 计算机应用研究, 2023, 40 (4): 1113-1118. (Shi Hongwei, Wang Zhichao, Shi Lianmin, et al. Linear coding-based federated learning [J]. Application Research of Computers, 2023, 40 (4): 1113-1118. )

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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