Special Topics in Federated Learning
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 3
688-693

Efficient and heterogeneous federated learning based on agent election

Wang Guanghui1,2
Bai Tianshui1
Ding Shuang1,2
He Xin1,2
1. School of Software, Henan University, Kaifeng Henan 475000, China
2. Henan International Joint Laboratory of Intelligent Network Theory & Key Technology, Kaifeng Henan 475000, China
DOI: 10.19734/j.issn.1001-3695.2023.06.0297

Abstract

The heterogeneity of diverse end devices in terms of computation, storage, and communication leads to insufficient accuracy and efficiency in federated learning. To address the issues faced in the aforementioned federated training process, this paper presented an efficient federated learning algorithm based on the idea of device agent election. To select agent nodes from diverse devices, it designed a device agent node election strategy based on Mahalanobis distance by considering the devices' computational capabilities and idle time as election factors to fully leverage their computing power. Furthermore, it proposed a novel cloud-edge-end federated learning architecture using the agent node to improve the efficiency of federated learning between heterogeneous devices. Experimental results based on the MNIST and CIFAR-10 public datasets and the practical smart home datasets demonstrate that the proposed efficient federated learning algorithm achieves average improvement about 22% in learning efficiency.

Foundation Support

中国博士后科学基金面上资助项目(2020M672217,2020M672211)
河南省重大科技专项(201300210400)
河南省重点研发与推广专项(科技攻关)(212102210094,222102210133,222102210055)
河南省高等学校重点科研项目(21A520003)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.06.0297
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 3
Section: Special Topics in Federated Learning
Pages: 688-693
Serial Number: 1001-3695(2024)03-007-0688-06

Publish History

[2023-09-06] Accepted Paper
[2024-03-05] Printed Article

Cite This Article

王光辉, 白天水, 丁爽, 等. 基于代理选举的高效异构联邦学习方法 [J]. 计算机应用研究, 2024, 41 (3): 688-693. (Wang Guanghui, Bai Tianshui, Ding Shuang, et al. Efficient and heterogeneous federated learning based on agent election [J]. Application Research of Computers, 2024, 41 (3): 688-693. )

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  • 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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