基于多领导者Stackelberg博弈的分层联邦学习激励机制设计

Incentive mechanism design for hierarchical federated learning based on multi-leader Stackelberg game

分层联邦学习中隐私安全与资源消耗等问题的存在降低了参与者的积极性。为鼓励足够多的参与者积极参与学习任务,并针对多移动设备与多边缘服务器之间的决策问题,提出基于多领导者Stackelberg博弈的激励机制。首先,通过量化移动设备的成本效用与边缘服务器的支付报酬,构建效用函数并定义最优化问题;其次,将移动设备之间的交互建模为演化博弈,将边缘服务器之间的交互建模为非合作博弈。为求解最优边缘服务器选择和定价策略,提出多轮迭代边缘服务器选择算法(MIES)和梯度迭代定价算法(GIPA),前者用于求解移动设备之间的演化博弈均衡解,后者用于求解边缘服务器之间的定价竞争问题。实验结果表明,所提算法GIPA与最优定价预测策略(OPPS)、历史最优定价策略(HOPS)和随机定价策略(RPS)相比,可使边缘服务器的平均效用分别提高4.06%、10.08%和31.39%。

The existence of privacy security and resource consumption issues in hierarchical federated learning reduces the enthusiasm of participants.To encourage a sufficient number of participants to actively participate in learning tasks and address the decision-making problem between multiple mobile devices and multiple edge servers,an incentive mechanism based on multi-leader Stackelberg game was proposed.Firstly,by quantifying the cost-utility of mobile devices and the payment of edge servers,a utility function was constructed,and an optimization problem was defined.Then,the interaction among mobile devices was modeled as an evolutionary game,and the interaction among edge servers was modeled as a noncooperative game.To solve the optimal edge server selection and pricing strategy,a Multi-round Iterative Edge Server selection algorithm(MIES)and a Gradient Iterative Pricing Algorithm(GIPA)were proposed.The former was used to solve the evolutionary game equilibrium solution among mobile devices,and the latter was used to solve the pricing competition problem among edge servers.Experimental results show that compared with Optimal Pricing Prediction Strategy(OPPS),Historical Optimal Pricing Strategy(HOPS)and Random Pricing Strategy(RPS),GIPA can increase the average utility of edge servers by 4.06%,10.08%,and 31.39%respectively.