Federated Learning Incentive Mechanism Design via Enhanced Shapley Value Method

Author:

Yang Xun1ORCID,Tan Weijie234ORCID,Peng Changgen23ORCID,Xiang Shuwen1ORCID,Niu Kun2ORCID

Affiliation:

1. College of Mathematics and Statistics, Guizhou University, Guiyang 550025, China

2. State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China

3. Guizhou Big Data Academy, Guizhou University, Guiyang 550025, China

4. Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China

Abstract

Federated learning (FL) is an emerging collaborative machine learning method. In FL processing, the data quality shared by users directly affects the accuracy of the federated learning model, and how to encourage more data owners to share data is crucial. In other words, how to design a good incentive mechanism is the key problem in FL. In this paper, we propose an incentive mechanism based on the enhanced Shapley value method for FL. In the proposed mechanism, the enhanced Shapley value method is proposed to measure income distribution, which takes multiple influence factors as weights. The analytic hierarchy process (AHP) is used to find the corresponding weight value of the influence factors. Finally, the numerical experiments are carried to verify the performance of the proposed incentive mechanism. The results show that compared with the Shapley value method considering the single factor, the income distribution of all participants can better reflect multiple factor contribution when using the enhanced Shapley value method.

Funder

Ministry of Education

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

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