Quantum Vanguard: Server Optimized Privacy Fortified Federated Intelligence for Future Vehicles

TL;DR

This paper introduces vQFL, a quantum federated learning framework for autonomous vehicles that enhances privacy and security using quantum techniques, while maintaining high model accuracy and efficiency.

Contribution

It presents a novel quantum federated learning framework with server-side fine-tuning and multi-layered security, tailored for autonomous vehicle networks in the post-quantum era.

Findings

vQFL maintains accuracy comparable to standard QFL.

Significantly improves privacy guarantees and communication security.

Minimal performance overhead with quantum security measures.

Abstract

This work presents vQFL (vehicular Quantum Federated Learning), a new framework that leverages quantum machine learning techniques to tackle key privacy and security issues in autonomous vehicular networks. Furthermore, we propose a server-side adapted fine-tuning method, ft-VQFL,to achieve enhanced and more resilient performance. By integrating quantum federated learning with differential privacy and quantum key distribution (QKD), our quantum vanguard approach creates a multi-layered defense against both classical and quantum threats while preserving model utility. Extensive experimentation with industry-standard datasets (KITTI, Waymo, and nuScenes) demonstrates that vQFL maintains accuracy comparable to standard QFL while significantly improving privacy guaranties and communication security. Our implementation using various quantum models (VQC, QCNN, and SamplerQNN) reveals minimal performance overhead despite the added security measures. This work establishes a crucial foundation for quantum-resistant autonomous vehicle systems that can operate securely in the post-quantum era while efficiently processing the massive data volumes (20-40TB/day per vehicle) generated by modern autonomous fleets. The modular design of the framework allows for seamless integration with existing vehicular networks, positioning vQFL as an essential component for future intelligent transportation infrastructure.