论文标题
用于联合学习的量子密钥分布（QKD）中的自适应资源分配
Adaptive Resource Allocation in Quantum Key Distribution (QKD) for Federated Learning
论文作者
论文摘要
智能本地6G网络中的隐私和安全问题的增加需要量子密钥分配保存的联合学习（QKD-FL），在这种情况下，通过量子通道连接的数据所有者可以在不公开其本地数据集的情况下进行FL Global Model进行训练。为了促进QKD-FL，建筑设计和路由管理框架至关重要。但是，仍然缺乏有效的实施。为此，我们为QKD-FL系统提出了一个层次结构，其中QKD资源（即波长）和路由共同针对FL应用进行了优化。特别是，我们专注于自适应QKD资源分配和FL工人的路由，以最大程度地减少QKD节点在各种不确定性（包括安全要求）下的部署成本。实验结果表明，与CO-QBN算法相比，提出的架构以及资源分配和路由模型可以将部署成本降低7.72 \％。
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.
