App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 8

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 Jiangsu 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 proposes a blockchain-based federated learning model aggregation approach that assigns the task of model aggregation to numerous miners instead of a single node. Miners propose various candidate aggregation solutions and generate corresponding blocks, then the main chain is determined based on the highest accuracy chain principle to achieve consensus among nodes. Additionally, to counteract malicious training nodes, a training node selection mechanism based on staking "training coins" is introduced, 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 achieved by this approach is respectively 8.64%, 19.89%, and 22.93% 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 Accepted Paper, Vol. 41, 2024 No. 8

Publish History

[2024-03-07] Accepted Paper

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). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.12.0604. (Luo Fulin, Chen Yunfang, Chen Xu, et al. Federated learning model aggregation scheme based on blockchain [J]. Application Research of Computers, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.12.0604. )

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.

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