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

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.

Figures (16)

• Figure 1: Channel Estimation Example data
• Figure 2: Radar Spectrum Example data
• Figure 3: QKD-secured federated learning system architecture. Clients train locally, mask updates with pairwise keys, and upload; server aggregates without seeing plain parameters.
• Figure 4: BB84 QKD protocol flow: state preparation, measurement, sifting, QBER check, and privacy amplification.
• Figure 5: Pairwise additive masking: each client adds masks for $j>i$ and subtracts for $j<i$; masks cancel at aggregation.