Physical-layer secure key distribution for dynamic fiber links using transfer learning

Abstract

Physical-layer secure key distribution (PLSKD) in fiber enhances security by utilizing channel dynamics to generate secure keys. However, PLSKD is significantly influenced by various channel scenarios. To improve the application of PLSKD, we investigate the use of transfer learning to enhance the performances. By leveraging the nonlinear mapping capabilities of neural networks using transfer learning, a high correlation coefficient and stable key generation rates across optical links of different fiber lengths are achieved, with lower computational requirements. Experimental results show that models pre-trained on a 100 km fiber link can be fine-tuned and effectively applied to a 300 km link, demonstrating excellent generalization and faster convergence with the same data volume. The proposed algorithm provides a practical solution for optimizing PLSKD performance in dynamic fiber links.