A Blockchain based Federated Learning for Message Dissemination in Vehicular Networks

TL;DR

This paper introduces a blockchain-based federated learning approach to improve message dissemination in vehicular networks, reducing delays, increasing delivery rates, and preserving privacy through an incentive mechanism.

Contribution

It proposes a novel blockchain-assisted federated learning framework with a Proof-of-Federated-Learning consensus, enhancing message dissemination efficiency and privacy in vehicular networks.

Findings

Reduces consensus delay by 65.2%

Increases message delivery rate by at least 8.2%

Enhances privacy preservation among vehicles

Abstract

Message exchange among vehicles plays an important role in ensuring road safety. Emergency message dissemination is usually carried out by broadcasting. However, high vehicle density and mobility usually lead to challenges in message dissemination such as broadcasting storm and low probability of packet reception. This paper proposes a federated learning based blockchain-assisted message dissemination solution. Similar to the incentive-based Proof-of-Work consensus in blockchain, vehicles compete to become a relay node (miner) by processing the proposed Proof-of-Federated-Learning (PoFL) consensus which is embedded in the smart contract of blockchain. Both theoretical and practical analysis of the proposed solution are provided. Specifically, the proposed blockchain based federated learning results in more number of vehicles uploading their models in a given time, which can potentially lead to a more accurate model in less time as compared to the same solution without using blockchain. It also outperforms the other blockchain approaches for message dissemination by reducing 65.2% of time delay in consensus, improving at least 8.2% message delivery rate and preserving privacy of neighbor vehicle more efficiently. The economic model to incentivize vehicles participating in federated learning and message dissemination is further analyzed using Stackelberg game model.