Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (3): 20-29.doi: 10.11896/jsjkx.230700194

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

Survey of Incentive Mechanism for Cross-silo Federated Learning

WANG Xin1,2, HUANG Weikou1, SUN Lingyun2   

  1. 1 College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
    2 College of Computer Science and Technology,Zhejiang University,Hangzhou 310058,China
  • Received:2023-07-25 Revised:2023-12-10 Online:2024-03-15 Published:2024-03-13
  • About author:WANG Xin,born in 1984,Ph.D,asso-ciate professor,master supervisor,is a member of CCF(No.11687M).His mainresearch interests include machine lear-ning,big data analysis and federated learning.
  • Supported by:
    National Key R&D Program of China(2020YFB0906000,2020YFB0906004) and Zhejiang University of Technology Science and Technology Project(KYY-HX-20220288,KYY-HX-20180649).

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Abstract: As a kind of distributed machine learning,federated learning effectively solves the problem of data sharing in big data era.Among them,cross-silo federated learning,as a type of federated learning in which institutions cooperate with each other,is obviously very important to design a reasonable incentive mechanism in the process of cross-silo cooperation.Based on the perspective of cross-silo cooperation,this paper makes a comprehensive analysis of the existing incentive mechanism of cross-silo fe-derated learning.Firstly,this paper introduces three basic problems in the process of cross-silo cooperation:high privacy,data he-terogeneity,and fairness.Then,it analyzes the incentive mechanism design methods under two different cross-silo cooperation models centered on the global model and centered on participants.Finally,it summarizes the several factors that affect the stable development of cross-silo cooperation:data evolution of participants,changes in the cooperative relationship of participants,and negative behaviors of participants,and looks forward to the future direction of cross-silo federal cooperation.

Key words: Cross-silo federated learning, Incentive mechanism, Cross-silo cooperation, Distributed machine learning, Privacy computing

CLC Number: 

  • TP181
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