标题： 基于斯塔克尔伯格博弈的数字孪生辅助非正交多址接入网络联邦学习性能优化 显示英文标题

标题： Stackelberg Game Based Performance Optimization in Digital Twin Assisted Federated Learning over NOMA Networks

摘要： 尽管在保护数据隐私方面具有优势，联邦学习（FL）仍然由于分布式客户端的有限计算资源和不可靠的无线通信环境而受到慢速节点问题的影响。 通过有效模拟分布式资源，数字孪生（DT）在缓解这一问题方面显示出巨大潜力。 在本文中，我们利用DT在非正交多址接入（NOMA）网络中的联邦学习框架中协助联邦学习训练过程，考虑来自客户端的模型更新上的恶意攻击。 提出了一种基于声誉的客户端选择方案，该方案考虑了客户端在多个方面的异质性，并有效缓解了联邦学习系统中的中毒攻击风险。 为了最小化所提出系统的总延迟和能耗，我们通过将客户端和服务器分别视为领导者和跟随者来构建一个斯塔克伯格博弈。 具体来说，领导者旨在最小化能耗，而跟随者的目的是在联邦学习训练期间最小化总延迟。 实现了斯塔克伯格均衡以获得最优解。 我们首先推导出跟随者层面问题的策略，并将其包含在领导者层面问题中，然后通过问题分解对其进行求解。 仿真结果验证了所提方案的优越性能。 显示英文摘要

摘要： Despite the advantage of preserving data privacy, federated learning (FL) still suffers from the straggler issue due to the limited computing resources of distributed clients and the unreliable wireless communication environment. By effectively imitating the distributed resources, digital twin (DT) shows great potential in alleviating this issue. In this paper, we leverage DT in the FL framework over non-orthogonal multiple access (NOMA) network to assist FL training process, considering malicious attacks on model updates from clients. A reputationbased client selection scheme is proposed, which accounts for client heterogeneity in multiple aspects and effectively mitigates the risks of poisoning attacks in FL systems. To minimize the total latency and energy consumption in the proposed system, we then formulate a Stackelberg game by considering clients and the server as the leader and the follower, respectively. Specifically, the leader aims to minimize the energy consumption while the objective of the follower is to minimize the total latency during FL training. The Stackelberg equilibrium is achieved to obtain the optimal solutions. We first derive the strategies for the followerlevel problem and include them in the leader-level problem which is then solved via problem decomposition. Simulation results verify the superior performance of the proposed scheme.