Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (3): 39-47.doi: 10.11896/jsjkx.230700186

• Information Security Protection in New Computing Mode • Previous Articles     Next Articles

Study on Blockchain Based Federated Distillation Data Sharing Model

LIU Wei1,2,3, LIU Yuzhao1,3, TANG Congke1,3, WANG Yuanyuan5, SHE Wei1,3,4, TIAN Zhao1,3   

  1. 1 School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450002,China
    2 Henan Key Laboratory of Network Cryptography Technology(Information Engineering University),Zhengzhou 450000,China
    3 Zhengzhou Key Laboratory of Blockchain and Data Intelligence(Zhengzhou University),Zhengzhou 450000,China
    4 Songshan Laboratory,Zhengzhou 450000,China
    5 State Grid Henan Electric Power Company,Xuchang,Henan 461000,China
  • Received:2023-07-24 Revised:2023-11-30 Online:2024-03-15 Published:2024-03-13
  • About author:LIU Wei,born in 1981,Ph.D,associate professor,Ph.D supervisor,is a member of CCF(No.49811M).His main research interests include blockchain technology,privacy protection and smart healthcare.TIAN Zhao,born in 1985,Ph.D, asso-ciate professor,is a member of CCF(No.K7436M).His main research interests include blockchain technology,information security,and intelligent transport.
  • Supported by:
    Henan Province University Science and Technology Innovation Talent Support Plan(21HASTIT031),Research Project of Henan Provincial Key Laboratory of Network Cryptography Technology(LNCT2022-A04), Key Scientific Research Project of Colleges and Universities in Henan Province(24A520045) and Songshan Laboratory Pre-research Project(YYYY022022003)

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Abstract: The privacy of raw data makes it difficult to be directly shared among multiple participants.The issue of data security sharing and privacy-preserving has become a hot research topic.To solve this problem,this paper proposes a blockchain-based bederated distillation data sharing model(BFDS).It utilizes blockchain to form a collaborative teacher network with multiple participants.Through distilled output exchange,the knowledge from complex teacher networks is transferred and used to train lightweight models.A novel multi-weight node trust evaluation algorithm is proposed that uses smart contracts to generate traceable global soft labels.It can reduce the negative impact caused by quality differences among participants.Experimental results show that BFDS can collaborate with multiple parties to share data knowledge reliably,distill training models collaboratively,and reduce model deployment costs.The proposed algorithm can effectively reduce the negative impact of low-quality nodes and improve the quality and security of global soft labels.

Key words: Blockchain, Knowledge distillation, Data sharing, Smart contracts

CLC Number: 

  • TP391
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