Breaking the Centralization Barrier: Exploring Decentralized Federated Learning for Vehicle Number Plate Recognition in IoV

The development of effective and safe machine learning systems for vehicle number plate recognition (VNPR) is now necessary due to the emergence of the Internet of Vehicles (IoV). However, traditional centralised techniques run into issues with data privacy, communication overhead, and centralised data access restrictions. This study explores the possibilities of decentralized federated learning for VNPR in the IoV to solve these constraints. Decentralised federated learning, which overcomes the centralization barrier, allows local model training on the edge devices of participating cars, protecting data privacy and cutting down on communication overhead. The ramifications of this paradigm change are examined in this research, including improved data privacy and security, shared intelligence, and resilience against errors and assaults. It also looks at the trade-off between performance and decentralisation while emphasising the balance attained via improved model aggregation and resource use. Additionally covered is the difficulty of consensus algorithms and blockchain-based networks, highlighting the need for further investigation and development. Decentralised federated learning has been identified as a possible strategy for overcoming the centralization barrier in VNPR systems, opening the door to the implementation of efficient, secure, and private machine learning in the IoV.

Keywords: Blockchain technology, Decentralization, Internet of Vehicles (IoV), Vehicle number plate recognition (VNPR).