Quantum Key Distribution-Enabled Secure Federated Learning with QBER-Based Attack Detection

Abstract

Federated Learning (FL) enables collaborative model learning without the need to share raw data, but its communication links are vulnerable to interception, replay, and man-in-the-middle (MITM) attacks. The existing key exchange methods rely on computational hardness assumptions, which can be broken by post-quantum attackers. In this paper, a secure federated learning framework improved by Quantum Key Distribution (QKD) is proposed, which integrates BB84-like quantum key generation and authenticated encryption on a per-round basis, as well as Quantum Bit Error Rate (QBER)-assisted intrusion detection. A new cryptographic key is produced in each federated round, making it immune to replay attacks and allowing for detection of tampering. Theoretical calculations show that intercept-resend attacks lead to a minimum expected QBER of 25%, making it easier to detect statistically. Experimental results on the MNIST dataset show near-perfect detection rates for MITM and replay attacks, with QBER values increasing from about 1% (serving as a benign scenario) to about 26% in an attack scenario. Communication overhead is kept below 10%, with negligible computational latency compared to local training. The experiments show that the use of QKD-based key refresh improves FL communication security while still ensuring model convergence.