首页|An Incentive Mechanism for Federated Learning:A Continuous Zero-Determinant Strategy Approach
An Incentive Mechanism for Federated Learning:A Continuous Zero-Determinant Strategy Approach
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
国家科技期刊平台
NETL
NSTL
万方数据
维普
As a representative emerging machine learning tech-nique,federated learning(FL)has gained considerable popular-ity for its special feature of"making data available but not visi-ble".However,potential problems remain,including privacy breaches,imbalances in payment,and inequitable distribution.These 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 interac-tion 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 FL.Finally,we perform simulations to demonstrate that our pro-posed CZD-based incentive mechanism can indeed generate high and stable social welfare in FL.
Changbing Tang、Baosen Yang、Xiaodong Xie、Guanrong Chen、Mohammed A.A.Al-qaness、Yang Liu
展开 >
College of Physics and Electronic Information Engineering,Zhejiang Normal University,Jinhua 321004,China
School of Mathematical Sciences,Zhejiang Normal University,Jinhua 321004,China
Department of Electrical Engineering,City University of Hong Kong,Hong Kong,China
Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province,and also with the School of Mathematical Sciences,Zhejiang Normal University,Jinhua 321004,China
展开 >
国家自然科学基金浙江省自然科学基金Jinhua Science and Technology Project
国家科技期刊平台
NSTL
万方数据
维普
