Editors: Shelly Gupta, Puneet Garg, Jyoti Agarwal, Hardeo Kumar Thakur, Satya Prakash Yadav

Series Title: Federated Learning for Internet of Vehicles: IoV Image Processing, Vision and Intelligent Systems

Federated Learning Based Intelligent Systems to Handle Issues and Challenges in IoVs - Part 2

Volume 3

eBook: US $49 Special Offer (PDF + Printed Copy): US $86
Printed Copy: US $61
Library License: US $196
ISBN: 978-981-5322-23-1 (Print)
ISBN: 978-981-5322-22-4 (Online)
Year of Publication: 2025
DOI: 10.2174/97898153222241250301

Introduction

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.

Preface

In an era where the Internet of Vehicles (IoVs) is altering our transportation environment, the demand for intelligent systems capable of effectively processing and analyzing massive volumes of data has never been more. The convergence of IoVs with powerful machine learning algorithms has opened up new opportunities to improve road safety, efficiency, and user experience. However, this rapid evolution presents its own set of obstacles, ranging from data privacy concerns to the intricacies of real-time decision-making.

By examining the cutting-edge federated learning paradigm, this book, Federated Learning Based Intelligent Systems to Handle Issues and Challenges in IoVs, aims to answer these urgent problems. Federated learning, in contrast to conventional centralized methods, permits decentralized data processing, allowing cars to jointly learn from local data while maintaining privacy. This approach not only reduces the hazards connected with data exchange, but also improves the adaptability of intelligent systems under a variety of driving situations.

We explore the major issues that IoVs are now confronting throughout this work, such as data heterogeneity, network latency, and the requirement for strong security measures. Each chapter mixes theoretical ideas with practical examples, showing how federated learning can be used to develop resilient, intelligent systems that can thrive in the dynamic environment of connected automobiles.

We encourage you to consider the revolutionary possibilities of these technologies as you set out on this journey through the nexus of federated learning and IoVs. Our hope is that this book will not only be a valuable resource for researchers and practitioners but will also stimulate more innovation in the sector, paving the way for smarter, safer transportation systems.

We are grateful to the authors, scholars, and practitioners who have contributed their skills to this work. We are building the foundation for a time when intelligent technologies prioritize privacy and safety over transportation.


Shelly Gupta
CSE (AI) Department
KIET Group of Institutions, U.P.,
Delhi-NCR Ghaziabad, India

Puneet Garg
Department of CSE-AI
KIET Group of Institutions, Ghaziabad, U.P., India

Jyoti Agarwal
CSE Department
Graphics Era University (Deemed to Be), India

Hardeo Kumar Thakur
School of Computer Science Engineering and Technology (SCSET)
Bennett University, Greater Noida
U.P., India

&

Satya Prakash Yadav
School of Computer Science Engineering and Technology (SCSET)
Bennett University, Greater Noida
U.P., India