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Technology of Blockchain
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 1
33-41

FedSharing：federated learning framework for data sharing driven by dual blockchain incentives

Chen Qiaosonga
Xu Wenjiea
He Xiaoyanga
Ding Xiaoyueb
Sun Kaiweia
Deng Xina
Wang Jina
a. Chongqing Key Laboratory of Data Engineering & Visual Computing, College of Computer Science & Technology, b. Smart Energy Technology Research Center, School of Automation/Industrial Internet College, Chongqing University of Posts & Telecommunications, Chongqing 400065, China
DOI: 10.19734/j.issn.1001-3695.2022.05.0277

Abstract

FL can achieve secure distributed model training based on privacy protection technology without losing data ownership, but it also has some problems such as centralization and lack of fair incentives. Blockchain is a distributed database, which has the characteristics of decentralization, trust and notarization, but it also has some key problems, such as low network throughput and waste of resources. Aiming at the problems and characteristics of the above technical methods, this paper proposed a federated learning framework for data sharing driven by dual blockchain incentives, named FedSharing. It constructed the main chain and side chain respectively. The main chain used transactions to encapsulate the global parameters exchanged in federated learning, and combined on-chain smart contracts and off-chain capacity expansion technology to establish gradient state channels. Side chain proposed a new modified Shapley value proof-of-work algorithm(PoFS), which modified the traditional Shapley value calculation premise of equality of members, and took into account the factor affecting the interests of the alliance, the integrity of the cooperation history of members in federated learning. The test results show that the time of each round of the gradient state channel is reduced by 4~5 s on average compared with the smart contract decentralized scheme, and the incentive allocation ratio under the PoFS consensus is more in line with the fair reality.

Foundation Support

基于校企协同的大数据智能应用方案研究(K2021-114)
重庆市研究生科研创新项目(CYS21311)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.05.0277
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 1
Section: Technology of Blockchain
Pages: 33-41
Serial Number: 1001-3695(2023)01-005-0033-09

Publish History

[2022-08-29] Accepted Paper
[2023-01-05] Printed Article

Cite This Article

陈乔松, 许文杰, 何小阳, 等. FedSharing：一种双区块链激励驱动的数据分享联邦学习框架 [J]. 计算机应用研究, 2023, 40 (1): 33-41. (Chen Qiaosong, Xu Wenjie, He Xiaoyang, et al. FedSharing：federated learning framework for data sharing driven by dual blockchain incentives [J]. Application Research of Computers, 2023, 40 (1): 33-41. )

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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