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

arXiv: https://arxiv.org/abs/2603.15649 · HTML: https://mlsanity.com/paper/2603.15649/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Builds on (most cited first, 1 of 1)

- [GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation](https://mlsanity.com/paper/2503.13180/) (2025)

## Built on by (most cited first, 1 of 1)

- [Efficient Secure Federated Learning via Information-Theoretically Secure Key Distribution: A Medical Imaging Case Study](https://mlsanity.com/paper/2610.06420/) (2026)

## Abstract (arXiv)

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.