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

TL;DR

This paper introduces a quantum key distribution-secured federated learning framework for 6G wireless channel estimation and radar sensing, ensuring privacy and security against eavesdroppers.

Contribution

It combines quantum key distribution with federated learning for secure, privacy-preserving channel and radar data analysis in next-generation networks.

Findings

Secure FL achieves NMSE of 0.216 for channel estimation.

Radar sensing accuracy reaches 92.1% with 0.72 mIoU.

Eavesdropper detection causes rounds to abort, maintaining security.

Abstract

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 $\sim$25\% and all rounds abort as intended; reconstruction error remains below $10^{-5}$, confirming correct aggregation.