现代信息科技2024，Vol.8Issue(22)：30-35,6.DOI:10.19850/j.cnki.2096-4706.2024.22.007
非独立同分布数据环境下的联邦学习激励机制设计
Design of Federal Learning Incentive Mechanism in Non-IID Data Environment
摘要
Abstract
In the Federated Learning environment,the existence of Non-Independent Identically Distributed(Non-IID)data poses a serious challenge to model performance and user engagement.To address these challenges,this paper proposes a new incentive mechanism based on game theory and Deep Reinforcement Learning,to improve the Federated Learning effect in Non-IID data environment.By designing the payofffunction of the central server and the user,considering the communication cost,computing cost and local model accuracy,the user contribution is measured fairly,and the user participation strategy is optimized by using the game theory model and the Deep Reinforcement Learning algorithm.The experimental results show that the proposed incentive mechanism significantly improves the accuracy of the model and the participation of users,and effectively alleviates the negative impact of Non-IID data distribution on Federated Learning performance,so as to enhance the performance and stability of the whole system.关键词
联邦学习/博弈论/非独立同分布/激励机制/深度强化学习Key words
Federated Learning/game theory/Non-IID/incentive mechanism/Deep Reinforcement Learning分类
信息技术与安全科学引用本文复制引用
李秋贤,周全兴..非独立同分布数据环境下的联邦学习激励机制设计[J].现代信息科技,2024,8(22):30-35,6.基金项目
黔东南州科技计划项目(黔东南科合J字[2023]106号) （黔东南科合J字[2023]106号）
2022年度凯里学院规划课题(2022YB08) （2022YB08）
扶持市(州)高校质量提升工程项目(院办发[2022]10号-32) （州）
贵州省科技计划项目(黔科合基础-ZK[2023]一般440) （黔科合基础-ZK[2023]一般440）
