Increasing privacy and security concerns in intelligence-native 6G networks require quantum key distribution-secured federated learning (QKD-FL), in which data owners connected via quantum channels can train an FL global model collaboratively without exposing their local datasets. To facilitate QKD-FL, the architectural design and routing management framework are essential. However, effective implementation is still lacking. To this end, we propose a hierarchical architecture for QKD-FL systems in which QKD resources (i.e., wavelengths) and routing are jointly optimized for FL applications. In particular, we focus on adaptive QKD resource allocation and routing for FL workers to minimize the deployment cost of QKD nodes under various uncertainties, including security requirements. The experimental results show that the proposed architecture and the resource allocation and routing model can reduce the deployment cost by 7.72\% compared to the CO-QBN algorithm.
翻译:6G网络中越来越多的隐私和安全问题要求量子关键分配安全化联合学习(QKD-FL),在这种学习中,通过量子渠道连接的数据拥有者可以在不暴露其本地数据集的情况下合作培训FL全球模型。为便利QKD-FL, 建筑设计和路由管理框架至关重要。然而,仍然缺乏有效的实施。为此,我们为QKD-FL系统提议一个等级结构,其中QKD资源(即波长)和路由在FL应用中联合优化。特别是,我们侧重于适应性QKD资源分配和FL工人的路线安排,以尽量减少包括安全要求在内的各种不确定因素下的QKD节点的部署费用。实验结果表明,拟议的架构和资源分配及路由模式可以比CO-QN算法减少部署费用7.72 ⁇ 。