Federated Learning Based Intelligent Systems to Handle Issues and Challenges in Iovs (Part 2).

Format:

Book

Author/Creator:

Gupta, Shelly.

Contributor:

Garg, Puneet.

Agarwal, Jyoti.

Series:

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

Language:

English

Subjects (All):

Federated searching.

Blockchains (Databases).

Physical Description:

1 online resource (245 pages)

Edition:

1st ed.

Place of Publication:

Sharjah : Bentham Science Publishers, 2025.

Summary:

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.

Contents:

Cover

Title

Copyright

End User License Agreement

Contents

Preface

List of Contributors

Federated Learning on Wheels: A Decentralized Approach to Privacy-Enhanced Data Collection in Internet of Vehicles

Neha Sharma1,*, Urvashi Sugandh2, Jyoti Agarwal3, Arvind Panwar2 and Priyanka Gaba4

INTRODUCTION

An Overview of the Internet of Vehicles (IOV) and the Difficulties it Faces in Collecting Data

Overview of Federated Learning as a Strategy for Improving Privacy

The Significance of Decentralised Data Collection in IOV

PRIVACY CONCERNS IN IOV DATA COLLECTION

Discussion of the Privacy Concerns and Sensitivity of IOV Data

Problems with Conventional Centralised Data-collecting Methods

An Introduction to Technologies that Increase Privacy

FEDERATED LEARNING: CONCEPTS AND PRINCIPLES

Overview of Federated Learning and its Decentralized Nature

Key Components of Federated Learning: Clients, Server, and Model Updates

Benefits of Federated Learning in Preserving Privacy in IOV

FEDERATED LEARNING ON WHEELS

Introduction to the Concept of FLOW

Utilizing On-board Computing Capabilities for Decentralized Model Training

Challenges and Considerations in Implementing FLOW in IOV

PRIVACY-ENHANCED DATA COLLECTION WITH FLOW

Privacy-preserving Data Collection Mechanisms in FLOW

Secure Aggregation Protocols for Preserving Privacy during Model Updates

Techniques for Ensuring Data Privacy and Confidentiality in FLOW

PERFORMANCE AND ACCURACY CONSIDERATIONS Generated by AI.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

981-5322-22-2

OCLC:

1519994377