Federated Learning for Internet of Vehicles: IoV Image Processing, Vision, and Intelligent Systems (Volume 3) explores how federated learning is revolutionizing the Internet of Vehicles (IoV) by enabling secure, decentralized, and scalable solutions. Combining theoretical insights with practical applications, this book addresses key challenges such as data privacy, heterogeneous information, and network latency in IoV systems.
This volume offers cutting-edge strategies to build intelligent, resilient vehicular systems, from privacy-enhanced data collection to blockchain-based payments, smart transportation systems, and vehicle number plate recognition. It highlights how federated learning drives advancements in secure data sharing, identity-based authentication, and real-time road safety improvements.
Key Features:
— In-depth exploration of federated learning applications in IoV.
— Solutions for privacy, security, and scalability challenges.
— Practical examples of blockchain integration and smart systems.
— Insights into future research directions for IoV.
Readership:
Ideal for researchers, graduate students, and practitioners in intelligent transportation, IoT, AI, and blockchain technologies.
