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

TL;DR

The paper tackles privacy and security challenges in autonomous-vehicle federated learning under looming quantum threats. It introduces vQFL, a framework that integrates quantum federated learning with differential privacy and BB84-based quantum key distribution, plus a server-side fine-tuning variant (ft-VQFL). Through Qiskit-based experiments on KITTI, Waymo, and nuScenes using VQC, QCNN, and SamplerQNN, the approach achieves competitive accuracy with enhanced privacy and security and minimal overhead. This work establishes a foundation for quantum-resistant, privacy-preserving collaborative learning in future intelligent transportation systems.

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.

Figures (22)

• Figure 1: Comprehensive Architecture of vQFL Framework for Privacy-Preserving Secure Autonomous Driving Intelligence.
• Figure 2: Proposed privacy-preserving secure vehicular QFL Framework: Autonomous vehicles collaboratively train global model; vQFL framework integrates quantum computing with autonomous vehicle technology. The architecture involves connected vehicles with onboard quantum processors training models locally on private driving data. Our three-tiered privacy and security approach combines: (1) Base-level QFL privacy where only model parameters are shared; (2) Differential privacy layer applying calibrated noise to parameters; and (3) Quantum cryptographic layer implementing BB84 protocol for secure communications. The system can be extended to both client-server and peer-to-peer topologies, enabling collaborative intelligence while maintaining quantum-resistant security.
• Figure 3: QKD Protocol: Standard BB84 protocol for Quantum Key Distribution bennettQuantumCryptographyPublic2014.
• Figure 4: QKD Generation: Key Generation implemented in this work using backend and following BB84 Protocol.
• Figure 5: QKD Encryption: Encryption and Decryption of model weights as performed in the work (sample preview)