FL-DECO-BC: A Privacy-Preserving, Provably Secure, and Provenance-Preserving Federated Learning Framework with Decentralized Oracles on Blockchain for VANETs

TL;DR

This paper introduces FL-DECO-BC, a blockchain-based federated learning framework for VANETs that ensures privacy, security, and provenance tracking, enabling trustworthy and secure traffic management applications.

Contribution

It presents a novel federated learning framework integrating decentralized oracles and cryptographic guarantees tailored for VANETs, enhancing security and data provenance.

Findings

Framework guarantees provable security via cryptography

Ensures data provenance and trustworthiness

Supports privacy-preserving machine learning in VANETs

Abstract

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.