Survey of Incentive Mechanism for Federated Learning

Abstract

Federated Learning(FL) is driven by multi-party data participation,where participants and central servers continuously exchange model parameters rather than directly upload raw data to achieve data sharing and privacy protection.In practical applications,the accuracy of the FL global model relies on multiple stable and high-quality clients participating,but there is an imba-lance in the data quality of participating clients,which can lead to the client being in an unfair position in the training process or not participating in training.Therefore,how to motivate clients to participate in federated learning actively and reliably is the key,which ensuring that FL is widely promoted and applied.This paper mainly introduces the necessity of incentive mechanisms in FL and divides the existing research into incentive mechanisms based on contribution measurement,client selection,payment allocation and multiple sub-problems optimization according to the sub-problems of incentive mechanisms in the FL training process,analyzes and compares existing incentive schemes,and summarizes the challenges in the development of incentive mechanisms on this basis,and explores the future research direction of FL incentive mechanisms.

Keywords

• federated learning|incentive mechanism|contribution measurement|client selection|payment allocation