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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 10
2968-2973

Device selection in federated learning under class imbalance

Wang Ximin
Fan Rui
School of Information Science & Technology, ShanghaiTech University, Shanghai 201210, China
DOI: 10.19734/j.issn.1001-3695.2021.03.0045

Abstract

Considering federated learning under mobile edge computing, where a global server connects a large number of mobile devices through the network to jointly train a deep neural network model. The data distribution shift caused by global class imbalance and device local class imbalance leads to the performance degradation of the standard federated averaging(FedAvg) algorithm. This paper proposed a device selection algorithm based on the combinatorial multi-armed bandit(CMAB) as an online learning algorithm framework, and designed a class estimation scheme to form a nonlinear reward function. Based on class estimation scheme and CMAB, in each round of communication, the global server selected the device subset with class imba-lance that could be best complementary with test performance deviation across classes of global model from last communication round. Therefore, the current aggregated global model could achieve more balance and better test performance per class, and also faster convergence and more stable training dynamics. Extensive numerical results demonstrate the influence of different parameters on FedAvg under class imbalance, and verify the effectiveness of the proposed algorithm.

Foundation Support

上海科技大学启动基金资助项目(2017F0203-000-05)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.03.0045
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 10
Section: Algorithm Research & Explore
Pages: 2968-2973
Serial Number: 1001-3695(2021)10-014-2968-06

Publish History

[2021-10-05] Printed Article

Cite This Article

王惜民, 范睿. 基于类别不平衡数据联邦学习的设备选择算法 [J]. 计算机应用研究, 2021, 38 (10): 2968-2973. (Wang Ximin, Fan Rui. Device selection in federated learning under class imbalance [J]. Application Research of Computers, 2021, 38 (10): 2968-2973. )

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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