Blockchain‐based federated learning approaches in internet of things applications

Abstract

AbstractThe Internet of Things (IoT) is a new well‐structured emerging technology with communication of smart devices using the 5G technology, infrastructures of roads, vehicles, smart cities, traffic systems and user applications. The IoT applications facilitate providing prompt emergency responses, and improved quality of vehicles, and road services, with cost‐effective activities in the intelligent transportation systems. Federated Learning (FL) enhances privacy and security in intelligent transportation systems and the Internet of Vehicles (IoV), using advanced prediction methods. Integrating blockchain with IoT, particularly in FL for transportation systems and IoV, bolsters security and data integrity. This approach keeps data local while only sharing model updates, enhancing privacy. Blockchain's transparency aids in efficient IoT collaboration, crucial for accountability. Its consensus algorithms further ensure network integrity, validating transactions and updates across devices, protecting against attacks, and fostering a transparent, collaborative environment. This comprehensive review paper delves into the innovative integration of blockchain technology with federated learning and the dynamic domain of IoV. It extensively analyzes the primary concepts, methodologies, and challenges associated with the deployment of FL in IoVs. This review presents a novel categorization examining three main types of blockchain‐based FL approaches vertical, horizontal, and decentralized each tailored to specific IoV communication scenarios like Vehicle‐to‐Vehicle (V2V), Vehicle‐to‐Infrastructure (V2I), and Vehicle‐to‐Cloud (V2C). It highlights FL applications in cyber‐attack detection, data sharing, traffic prediction, and privacy, considering Quality of Service factors. Finally, some main challenges and new open issues are discussed and assessed for federated machine learning approaches in the IoV.