Nautilus: A Verifiable Hierarchical Federated Learning Framework for Vehicular-Edge-Cloud Systems

2026/06/30 by Linyang Wu, Linpeng Jia, Hanwen Zhang +2
#cs.DC

paper · pdf

Abstract

Federated Learning (FL) enables privacy-preserving collaborative learning for Internet of Vehicles (IoV) scenarios, but extreme heterogeneity of vehicular-edge-cloud resources severely limits system efficiency. Dynamic scheduling strategies mitigate this issue but introduce new trust concerns: verifying fair scheduling decisions and faithful client execution of compression instructions without privacy leakage remains an open challenge. We propose Nautilus, a verifiable efficient federated learning framework. First, a multi-dimensional resource-aware scheduling algorithm dynamically allocates compression ratios and training tasks based on vehicle bandwidth, latency and computing power, improving training efficiency. Second, a Zero-Knowledge Proof (ZKP) mechanism ensures scheduling fairness and execution compliance while preserving privacy. Experiments show the framework reduces communication overhead and accelerates convergence with guaranteed system integrity.

Citations

• Zero-Knowledge Proof-based Practical Federated Learning on Blockchain
• Free-rider Attacks on Model Aggregation in Federated Learning
• Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles
• TiFL: A Tier-based Federated Learning System
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey
• Federated Optimization in Heterogeneous Networks

Related

• Clustered Vehicular Federated Learning: Process and Optimization
• FLAD: Federated Learning for LLM-based Autonomous Driving in Vehicle-Edge-Cloud Networks
• Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems
• HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning
• A Dispersed Federated Learning Framework for 6G-Enabled Autonomous Driving Cars