TY - GEN
T1 - Adaptive resource allocation in Quantum Key Distribution (QKD) for federated learning
AU - Kaewpuang, Rakpong
AU - Xu, Minrui
AU - Niyato, Dusit
AU - Yu, Han
AU - Xiong, Zehui
AU - Shen, Xuemin Sherman
PY - 2023/3/23
Y1 - 2023/3/23
N2 - 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.
AB - 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.
KW - adaptive resource allocation
KW - Federated learning
KW - quantum key distribution
KW - stochastic programming
U2 - 10.1109/ICNC57223.2023.10074279
DO - 10.1109/ICNC57223.2023.10074279
M3 - Conference contribution
AN - SCOPUS:85152049772
T3 - International Conference on Computing, Networking and Communications (ICNC): Proceedings
SP - 71
EP - 76
BT - 2023 International Conference on Computing, Networking and Communications, ICNC 2023: Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Computing, Networking and Communications, ICNC 2023
Y2 - 20 February 2023 through 22 February 2023
ER -