An Incentive Mechanism for Federated Learning:A Continuous Zero-Determinant Strategy Approach

作     者：Changbing Tang Baosen Yang Xiaodong Xie Guanrong Chen Mohammed A.A.Al-qaness Yang Liu Changbing Tang;Baosen Yang;Xiaodong Xie;Guanrong Chen;Mohammed A.A.Al-qaness;Yang Liu

作者机构：IEEE the College of Physics and Electronic Information EngineeringZhejiang Normal UniversityUniversityJinhua 321004China the School of Mathematical SciencesZhejiang Normal UniversityJinhua 321004China the Department of Electrical EngineeringCity University of Hong KongHong KongChina Key Laboratory of Intelligent Education Technology and Application of Zhejiang ProvinceJinhua 321004China 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2024年第11卷第1期

页      面：88-102页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：partially supported by the National Natural Science Foundation of China (62173308) the Natural Science Foundation of Zhejiang Province of China (LR20F030001) the Jinhua Science and Technology Project (2022-1-042) 

主　　题：Federated learning(FL) game theory incentive mechanism machine learning zero-determinant strategy 

摘      要：As a representative emerging machine learning technique, federated learning(FL) has gained considerable popularity for its special feature of “making data available but not visible. However, potential problems remain, including privacy breaches, imbalances in payment, and inequitable *** shortcomings let devices reluctantly contribute relevant data to, or even refuse to participate in FL. Therefore, in the application of FL, an important but also challenging issue is to motivate as many participants as possible to provide high-quality data to FL. In this paper, we propose an incentive mechanism for FL based on the continuous zero-determinant(CZD) strategies from the perspective of game theory. We first model the interaction between the server and the devices during the FL process as a continuous iterative game. We then apply the CZD strategies for two players and then multiple players to optimize the social welfare of FL, for which we prove that the server can keep social welfare at a high and stable level. Subsequently, we design an incentive mechanism based on the CZD strategies to attract devices to contribute all of their high-accuracy data to ***, we perform simulations to demonstrate that our proposed CZD-based incentive mechanism can indeed generate high and stable social welfare in FL.