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 analysing 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 it 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.