Blockchain and Federated Learning in IoV

Abstract

The convergence of blockchain and federated learning (FL) will be immensely beneficial for the Internet of Vehicles (IoV) in addressing some of the major concerns such as data security, privacy, scalability, and real-time decision-making. IoV systems architecture inherently creates extensive sensitive data, which is susceptible to cyberattacks, as well as latency and other inefficiency from classical central architectures. By deploying smart contracts and cryptographic to keep the brood peered between vehicular devices, the decentralized nature of blockchain technology allows secure and immutable sharing of vehicular data, removes single point of failure and enhances the trust. So, as opposed to FL, federated learning is a general approach of artificial intelligence model that enhances the vehicle through training upon decentralized data shape; doing this avoids reveling of raw data and massively reduces the network traffic as well as lessening of data leakage. Real-world case studies are discussed in this chapter, which presents the applications of blockchain and FL in the IoV. Levinson, J. et al . and Kilic, S.M. et al ., provide examples of how these decentralized trust mechanisms minimize the security risks and improve the system performance in how they apply their blockchain-based approaches to secure vehicle data sharing, prevent fraud in vehicular insurance, and automate toll collections. Traffic prediction problems from FL-enabled applications for anomaly detection in self-driving and personalized driver assistance applications are also presented, highlighting that FL may even optimize real-time decisions and reduce computational overhead, as well as enhance model accuracy. The comparative analysis shows the advantages and disadvantages for both approaches while shedding light on the contribution of blockchain systems in terms of security and trust management and the outstanding features of FL concerning in situ intelligence and data privacy. In addition, this chapter explores the potential synergies between blockchain and FL, advocating for a hybrid paradigm in which blockchain improves the security and integrity of FL by means of decentralized model aggregation and verifiable learning processes. These findings indicate that the convergence of blockchain and FL can vastly enhance IoV applications by providing secured, scalable, and privacy-preserved vehicular networks in a distributed way. The areas that require future investigation include blockchain scalability, on-chain FL model aggregation techniques, and the need to combine these two technologies in conjunction with 5G and edge computing to construct future IoV ecosystems.