Special Topics in Federated Learning
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 3
700-705,720

Optimization selection and incentives of client in online asynchronous federated learning

Gu Yonggen1,2
Feng Zhouyang1
Wu Xiaohong1,2
Tao Jie1
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.1001-3695.2023.08.0333

Abstract

Federated learning enables different clients to collaborate and train a shared model while preserving user privacy. Motivating high-quality clients to participate in federated learning is crucial. In online federated learning environments, clients join and leave training dynamically, evaluating and selecting clients in real-time poses a challenge. To address this challenge, this paper proposed an online federated learning incentive algorithm to optimize client selection and budget allocation, thereby enhancing the performance of federated learning under budget constraints. The proposed algorithm divided the budget into stages and computed optimal quality density thresholds based on historical sample information. The main idea was to dynamically assess the quality of client models and employ a quality threshold admission mechanism while limiting the number of participating clients. In theory, this paper proved that the incentive algorithm satisfied incentive compatibility, budget feasibility, and individual rationality. Experimental results demonstrate that the proposed online incentive algorithm achieves good performance in scenarios with different proportions of free-riding clients. Specifically, compared to existing methods, it achieves approximately 4% and 10% improvements on the EMNIST-B and CIFAR-10 datasets, respectively, under sufficient budget and in the presence of free-riding and mislabeled clients.

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.1001-3695.2023.08.0333
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 3
Section: Special Topics in Federated Learning
Pages: 700-705,720
Serial Number: 1001-3695(2024)03-009-0700-06

Publish History

[2023-10-12] Accepted Paper
[2024-03-05] Printed Article

Cite This Article

顾永跟, 冯洲洋, 吴小红, 等. 在线异步联邦学习的客户优化选择与激励 [J]. 计算机应用研究, 2024, 41 (3): 700-705,720. (Gu Yonggen, Feng Zhouyang, Wu Xiaohong, et al. Optimization selection and incentives of client in online asynchronous federated learning [J]. Application Research of Computers, 2024, 41 (3): 700-705,720. )

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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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