Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 11
3385-3389

Incentive mechanism for federated learning with budget constraints under unbalanced data

Gu Yonggen1
Zhong Haotian1
Wu Xiaohong1
Tao Jie1
Zhang Yanqiong2
1. School of Information Engineering, Huzhou University, Huzhou Zhejiang 313000, China
2. School of Science & Engineering, Huzhou College, Huzhou Zhejiang 313000, China
DOI: 10.19734/j.issn.1001-3695.2022.04.0182

Abstract

Federal learning solves the problem of completing multi-customer cooperative machine learning under privacy protection, and incentivizing customers to participate in federated learning is an important prerequisite for model performance improvement. To address the impact of non-independent identically distribution of customer data on the performance of federated learning, this paper designed a client selection method based on unit data cost and data characteristics-EMD distance considering budget constraints, which theoretically proved that the mechanism had honesty, i. e., each customer would honestly disclose data cost and data distribution information, while the mechanism had budget feasibility, personal rationality and computational validity. The experimental results show that the accuracy of the model under the proposed incentive mechanism can reach at least 94% of that under the optimal choice of data volume(without considering the incentive) on average, and improve the model accuracy by more than 5% on average compared with the incentive mechanism without considering the data distribution characteristics.

Foundation Support

国家自然科学基金资助项目(61906066)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.04.0182
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 11
Section: Algorithm Research & Explore
Pages: 3385-3389
Serial Number: 1001-3695(2022)11-029-3385-05

Publish History

[2022-06-24] Accepted Paper
[2022-11-05] Printed Article

Cite This Article

顾永跟, 钟浩天, 吴小红, 等. 不平衡数据下预算限制的联邦学习激励机制 [J]. 计算机应用研究, 2022, 39 (11): 3385-3389. (Gu Yonggen, Zhong Haotian, Wu Xiaohong, et al. Incentive mechanism for federated learning with budget constraints under unbalanced data [J]. Application Research of Computers, 2022, 39 (11): 3385-3389. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)