SPARE: Selective parameter exchange for efficient cooperative learning in vehicular networks

In vehicular networks, decentralized cooperative learning strategies have gained significant attention due to the lower communication overhead they involve when compared to centralized cooperative learning approaches like Federated Learning. Decentralized solutions enable vehicles to collaboratively train Machine Learning (ML) models by exchanging parameters without relying on a central server. However, conventional model-sharing methods still suffer from high communication overhead and increased vulnerability to poisoning attacks.

This paper presents SPARE, a gossip-based cooperative learning protocol that leverages Vehicle-to-Vehicle (V2V) communication to enhance communication efficiency by exchanging selected model parameters. SPARE selects vehicle nodes for model updates and transmits only the most significantly updated layers, reducing redundancy and improving efficiency. This selective exchange minimizes communication resource consumption and enhances privacy, as the complete model is never shared across the network. We assess the proposed approach using a real-world driving dataset, featuring data from multiple drivers along the same route. Experimental results prove that our method achieves efficient learning with significantly lower communication overhead, demonstrating its suitability for deployment in resource-constrained vehicular networks.