This paper presents a federated learning framework secured by quantum key distribution (QKD) for wireless channel estimation and radar spectrum sensing in the next generation networks (NextG or Beyond 6G). A BB84-style protocol abstraction and pairwise additive masking are utilized to train clients' local models (CNN for channel estimation, U-Net for radar segmentation) and upload only masked model updates. The server aggregates without observing plain parameters; an eavesdropper without QKD keys cannot recover individual updates. Experiments show that secure FL achieves NMSE of 0.216 for channel estimation and 92.1\% accuracy with 0.72 mIoU for radar sensing. When an eavesdropper is present, QBER rises to ∼25\% and all rounds abort as intended; reconstruction error remains below 10−5, confirming correct aggregation.
@article{arxiv.2603.15649,
title = {Quantum Key Distribution Secured Federated Learning for Channel Estimation and Radar Spectrum Sensing in 6G Networks},
author = {Ferhat Ozgur Catak and Murat Kuzlu and Jungwon Seo and Umit Cali},
journal= {arXiv preprint arXiv:2603.15649},
year = {2026}
}