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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 8
2427-2433

Optimization of federated learning aggregation algorithm based on model quality scoring

Wu Xiaohong1,2
Lu Haonan1
Gu Yonggen1,2
Tao Jie1,2
1. School of Information Engineering, Huzhou University, Huzhou Zhejiang 313000, China
2. Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, Huzhou Zhejiang 313000, China
DOI: 10.19734/j.issn.1001-3695.2023.11.0586

Abstract

In federated learning environments, it is crucial to assess the quality of client data, especially when a validation set is not available. Traditional evaluation methods rely on measuring the loss of client models on the validation set of a central node to assess data quality. To address these issues, this paper proposed a method for scoring model quality based on peer information. This method involved tailoring the model parameters uploaded by the client and designing a model quality scoring mechanism based on the theories of correct scoring rules. It developed an optimized aggregation algorithm, leveraging the scores of clients to mitigate the impacts of low-quality local models on the global model. Experiments conducted on datasets like MNIST, Fashion-MNIST, and CIFAR-10 demonstrate that the proposed scoring mechanism is straightforward and effective in identifying three types of low-quality data clients: free-riding clients, overly privacy-protective clients, and mislabeled clients. The proposed method enhances the robustness of federated learning performance.

Foundation Support

湖州市科技计划重点研发计划资助项目(2022ZD2002)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0586
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 8
Section: Algorithm Research & Explore
Pages: 2427-2433
Serial Number: 1001-3695(2024)08-025-2427-07

Publish History

[2024-04-08] Accepted Paper
[2024-08-05] Printed Article

Cite This Article

吴小红, 陆浩楠, 顾永跟, 等. 基于模型质量评分的联邦学习聚合算法优化 [J]. 计算机应用研究, 2024, 41 (8): 2427-2433. (Wu Xiaohong, Lu Haonan, Gu Yonggen, et al. Optimization of federated learning aggregation algorithm based on model quality scoring [J]. Application Research of Computers, 2024, 41 (8): 2427-2433. )

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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