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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 1
139-148

Match-based model offloading for edge federated learning

Gu Yonggen
Zhang Lyuji
Wu Xiaohong
Tao Jie
School of Information Engineering, Huzhou University, Huzhou Zhejiang 313000, China
DOI: 10.19734/j.issn.1001-3695.2024.06.0199

Abstract

Aiming at problems such as the "straggler effect" caused by resource heterogeneity in federated learning in edge computing environments, this paper proposed a match-based model offloading for edge federated learning(Fed-MBMO). This method collected performance analysis results of edge devices, divided devices into strong and weak clients, and considered the time proportion of the four phases of model training, weak clients saved the time of backpropagation on the feature layers by freezing part of the model, and offload the model to the strong client for additional training, finally, the strong clients' feature layers were then reconstructed with the weak clients' fully connected layers. In order to improve the efficiency of model offloading, the offloading cost matrix is constructed by comprehensively considering the similarity of model feature layers and task completion time, and transform the problem into an iterative solution of the optimal matching problem based on bipartite graph, the proposed approach used a KM-based model offloading algorithm and further analyzed the time complexity of the Fed-MBMO algorithm. Experimental results show that in the case of extremely heterogeneous resources and datasets, this method can accelerate model convergence, and the model training time can be reduced by an average of 46.65 percent, 12.66 percent and 38.07 percent compared to FedAvg, FedUE and Aergia, respectively. The experimental results show that the Fed-MBMO algorithm can effectively solve the "straggler effect "problem and significantly improve the efficiency of federated learning.

Foundation Support

湖州市科技计划重点研发资助项目(2022ZD2002)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.06.0199
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 1
Section: Algorithm Research & Explore
Pages: 139-148
Serial Number: 1001-3695(2025)01-020-0139-10

Publish History

[2025-01-05] Printed Article

Cite This Article

顾永跟, 张吕基, 吴小红, 等. 基于匹配的模型卸载边缘联邦学习方法 [J]. 计算机应用研究, 2025, 42 (1): 139-148. (Gu Yonggen, Zhang Lyuji, Wu Xiaohong, et al. Match-based model offloading for edge federated learning [J]. Application Research of Computers, 2025, 42 (1): 139-148. )

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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