斯塔克伯格竞赛
激励
机构设计
机制(生物学)
计算机科学
透视图(图形)
博弈论
纳什均衡
质量(理念)
产业组织
微观经济学
业务
人工智能
经济
认识论
哲学
作者
Wei Guo,Yijin Wang,Pingyu Jiang
标识
DOI:10.1016/j.cie.2023.109592
摘要
Federated Learning (FL) is a typical decentralized Machine Learning framework in which clients invest resources to train their local models without sharing their data and then transmit the model parameters to the server for parameter aggregation. Therefore, Incentive Mechanism Design (IMD) is a basic and important research direction in FL that stimulates clients to invest more resources in model training. In this paper, we designed the incentive mechanism as a Stackelberg game in which the server acts as the leader and the clients act as followers. We leveraged modified NSGA-II to find the Nash equilibrium. A real industrial scenario of pre-baked carbon anode quality prediction is applied to verify the high performance of the proposed method.
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