Author: Mykyta Luzan
Supervisors: Mubashar Iqbal, Raimundas Matulevičius
Abstract: The Internet of Vehicles enables connected vehicles to share data and collaboratively learn to enhance road safety and traffic efficiency. Federated learning has emerged as a promising approach for enabling privacy-preserving collaborative learning among vehicles, allowing them to jointly train machine learning models without sharing raw sensitive data. However, the centralized architecture commonly used in federated learning introduces significant security vulnerabilities that can compromise system integrity and reliability. While extensive research exists on federated learning security in general, there is insufficient analysis of how these security challenges manifest in specific application contexts, particularly in dynamic environments like IoV. Here we show that integrating Hyperledger Fabric’s permissioned blockchain with zero-knowledge proofs creates a comprehensive security framework that effectively protects federated learning systems against both model tampering, aggregation protocol violation, and unauthorized access while maintaining privacy. Our systematic analysis and implementation reveals that blockchain technology can address core vulnerabilities in centralized federated learning architectures while preserving their privacy benefits, demonstrating advantages over previous approaches that relied solely on cryptographic protocols or trusted third parties. By validating our framework through a concrete IoV data sharing implementation, we establish a practical foundation for securing federated learning in distributed environments. The implications of this research extend beyond vehicular networks to any domain requiring secure collaborative learning among distributed participants. As autonomous systems become increasingly interconnected, this work demonstrates how combining blockchain with federated learning can enable trustworthy data sharing while preserving both privacy and security.