In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Technology of Blockchain
|
App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 8
2277-2283

Federated learning model aggregation scheme based on blockchain

Luo Fulin
Chen Yunfang
Chen Xu
Zhang Wei
School of Computer Science, Nanjing University of Posts & Telecommunications, Nanjing 210023, China
DOI: 10.19734/j.issn.1001-3695.2023.12.0604

Abstract

Traditional centralized federated learning relies on a trusted central server for model aggregation, creating a vulnerability to single-point failures. In contrast, existing decentralized federated learning schemes elect a node temporarily in each iteration cycle to aggregate the model, but cannot ensure the complete trustworthiness of the elected node. To solve the aforementioned issues, this paper proposed a blockchain-based federated learning model aggregation approach that assigned the task of model aggregation to numerous miners instead of a single node. Miners proposed various candidate aggregation solutions and generated corresponding blocks, then it determined the main chain based on the highest accuracy chain principle to achieve consensus among nodes. Additionally, to counteract malicious training nodes, it introduced a training node selection mechanism based on staking "training coins", allowing nodes to participate in training by staking "training coins", with the system rewarding or penalizing them based on their contribution to the model. Simulation results demonstrate that with 10%, 20%, and 30% malicious nodes in the system, the accuracy of this approach is respectively 8.64, 19.89, and 22.93 percent point higher than that of the federated averaging(FedAvg) scheme, and it also performs well in non-IID data training scenarios. In conclusion, this approach enhances the credibility of the federated learning aggregation process and ensures the effectiveness of federated learning training.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.12.0604
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 8
Section: Technology of Blockchain
Pages: 2277-2283
Serial Number: 1001-3695(2024)08-005-2277-07

Publish History

[2024-03-07] Accepted Paper
[2024-08-05] Printed Article

Cite This Article

罗福林, 陈云芳, 陈序, 等. 基于区块链的联邦学习模型聚合方案 [J]. 计算机应用研究, 2024, 41 (8): 2277-2283. (Luo Fulin, Chen Yunfang, Chen Xu, et al. Federated learning model aggregation scheme based on blockchain [J]. Application Research of Computers, 2024, 41 (8): 2277-2283. )

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)