Quantum Key Distribution Secured Federated Learning for Channel Estimation and Radar Spectrum Sensing in 6G Networks

Computer Science > Cryptography and Security [Submitted on 5 Mar 2026] Quantum Key Distribution Secured Federated Learning for Channel Estimation and Radar Spectrum Sensing in 6G Networks Ferhat Ozgur Catak, Murat Kuzlu, Jungwon Seo, Umit Cali 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 \sim25\% and all rounds abort as intended; reconstruction error remains below 10^{-5}, confirming correct aggregation. Comments: 10 pages Subjects: Cryptography and Security (cs.CR); Information Theory (cs.IT); Machine Learning (cs.LG) Cite as: arXiv:2603.15649 [cs.CR]   (or arXiv:2603.15649v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.15649 Focus to learn more Submission history From: Ferhat Ozgur Catak [view email] [v1] Thu, 5 Mar 2026 08:50:27 UTC (1,635 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.IT cs.LG math math.IT References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)