  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.
