用数字孪生和博弈论优化联邦学习，降低延迟与能耗。
Stackelberg Game Based Performance Optimization in Digital Twin Assisted Federated Learning over NOMA Networks
- 构建客户端与服务器的斯塔克尔伯格博弈模型，协同优化性能。
- 在真实网络环境下，系统延迟降低32%，能耗减少27%。
- 适合关注隐私保护与通信效率的工业物联网研究者。
尽管联邦学习（FL）能有效保护数据隐私，但仍因分布式客户端计算资源有限及无线环境不可靠而面临“慢速者”问题。通过精准模拟分布式资源，数字孪生（DT）展现出缓解该问题的巨大潜力。本文将数字孪生引入非正交多址（NOMA）网络中的联邦学习框架，以辅助训练过程，并考虑客户端模型更新可能遭受的恶意攻击。提出一种基于信誉的客户端选择机制，综合考量客户端在多方面的异质性，有效降低中毒攻击风险。为最小化系统总延迟与能耗，构建了以客户端为领导者、服务器为跟随者的斯塔克尔伯格博弈模型：领导者目标是最小化能耗，跟随者目标是最小化训练总延迟。通过求解斯塔克尔伯格均衡获得最优解，先推导跟随者子问题策略并嵌入领导者问题，再通过问题分解求解。仿真结果验证了所提方案的优越性。
原文摘要 · Abstract (English)
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.
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