Vehicular Ad-Hoc Networks (VANETs) hold immense potential for improving traffic safety and efficiency. However, traditional centralized approaches for machine learning in VANETs raise concerns about data privacy and security. Federated Learning (FL) offers a solution that enables collaborative model training without sharing raw data. This paper proposes FL-DECO-BC as a novel privacy-preserving, provably secure, and provenance-preserving federated learning framework specifically designed for VANETs. FL-DECO-BC leverages decentralized oracles on blockchain to securely access external data sources while ensuring data privacy through advanced techniques. The framework guarantees provable security through cryptographic primitives and formal verification methods. Furthermore, FL-DECO-BC incorporates a provenance-preserving design to track data origin and history, fostering trust and accountability. This combination of features empowers VANETs with secure and privacy-conscious machine-learning capabilities, paving the way for advanced traffic management and safety applications.

翻译：车载自组织网络（VANETs）在提升交通安全与效率方面具有巨大潜力。然而，传统的集中式机器学习方法在VANETs中引发了数据隐私与安全方面的担忧。联邦学习（FL）提供了一种无需共享原始数据即可实现协同模型训练的解决方案。本文提出FL-DECO-BC，这是一种专为VANETs设计的新型隐私保护、可证明安全且来源可追溯的联邦学习框架。FL-DECO-BC利用区块链上的去中心化预言机安全地访问外部数据源，同时通过先进技术确保数据隐私。该框架通过密码学原语和形式化验证方法保证可证明的安全性。此外，FL-DECO-BC融合了来源可追溯的设计，以追踪数据的起源与历史，从而增强信任与可问责性。这些特性的结合为VANETs提供了安全且注重隐私的机器学习能力，为先进的交通管理与安全应用铺平了道路。