Survey of Incentive Mechanism for Cross-silo Federated Learning

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.