基于Stackelberg博弈的异步联邦学习激励机制设计

Design of Incentive Mechanism for Asynchronous Federated Learning Based on Stackelberg Game

联邦学习作为能够保护数据隐私和保证数据安全的新型分布式机器学习被广泛关注,异步联邦学习作为传统联邦学习的变种,能有效提高模型训练效率。激励机制的引入能够帮助异步联邦学习有效提高模型训练效用。利用Stackelberg博弈构建了一个联邦学习激励机制,分别对中心服务器和数据拥有者效用进行优化。在此基础上,推导出了整个博弈的均衡解,最后通过算例分析了模型的可行性,得到最优的激励效果。

Federated Learning,as a new type of distributed machine learning that can protect data privacy and ensure data security,has received widespread attention.Asynchronous Federated Learning,as a variant of traditional Federated Learning,can effectively improve model training efficiency.The introduction of incentive mechanisms can help asynchronous Federated Learning effectively improve model training effectiveness.A federated learning incentive mechanism is constructed using the Stackelberg game,which optimizes the effectiveness of the central server and data owner.On this basis,we derive the equilibrium solution of the entire game,analyze the feasibility of the model through numerical examples finally,and obtain the optimal incentive effect.