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

2025-12-02

AI Summary

Autonomous driving networks face critical privacy and security challenges in the quantum computing era, driven by massive in-vehicle data generation (20–40 TB/vehicle/day), necessitating quantum-resistant cryptography, strong privacy guarantees, and efficient collaborative learning. Method: This paper proposes vQFL—a novel quantum-secure intelligent framework integrating quantum federated learning (QFL), differential privacy (DP), and quantum key distribution (QKD). It introduces an ft-VQFL server-side fine-tuning mechanism to balance multi-layered security and model utility, and incorporates variational quantum circuits (VQCs) and quantum convolutional neural networks (QCNNs) for modular deployment. Results: Experiments on KITTI, Waymo, and nuScenes demonstrate that vQFL achieves accuracy comparable to baseline QFL while significantly enhancing privacy preservation and communication security, with negligible computational overhead.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Adversarial Attacks & Robustness
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Enhances privacy and security in autonomous vehicular networks using quantum machine learning.
• Integrates differential privacy and quantum key distribution to defend against classical and quantum threats.
• Maintains model accuracy and performance while securing massive data volumes in future vehicles.

Innovation

• Quantum federated learning with differential privacy
• Server-side fine-tuning for resilient performance
• Quantum key distribution for multi-layered security