Rani Astya – författare

The book presents the latest developments in intelligent communication networks based on applicability from various domains of artificial intelligence and machine learning including channel modeling, model-based structure, channel prediction, and signal detection. It further explains important topics such as vehicular mobility modeling, human-centric network applications, security and privacy in social networks, and trust-based intelligent transportation systems. This book:Presents a model-based approach to constructing an effective network by using state-of-the-art artificial intelligent techniques. Discusses the theoretical and practical applications of channel prediction and signal detection. Introduces the fundamental concepts and application of vehicular networks in conjunction with artificial intelligence. Explores wireless communication network techniques enabled by human-centric applications, designed, and developed with artificial intelligence characteristics. Highlights the challenges in designing and developing an effective and intelligent communication network that can be applied in different domains of human activities for finding sustainable solutions.It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer engineering, and information technology.

The effectiveness of federated learning in high‑performance information systems and informatics‑based solutions for addressing current information support requirements is demonstrated in this book. To address heterogeneity challenges in Internet of Things (IoT) contexts, Federated Learning for Smart Communication using IoT Application analyses the development of personalized federated learning algorithms capable of mitigating the detrimental consequences of heterogeneity in several dimensions. It includes case studies of IoT‑based human activity recognition to show the efficacy of personalized federated learning for intelligent IoT applications.Features:Demonstrates how federated learning offers a novel approach to building personalized models from data without invading users’ privacyDescribes how federated learning may assist in understanding and learning from user behavior in IoT applications while safeguarding user privacyPresents a detailed analysis of current research on federated learning, providing the reader with a broad understanding of the areaAnalyses the need for a personalized federated learning framework in cloud‑edge and wireless‑edge architecture for intelligent IoT applicationsComprises real‑life case illustrations and examples to help consolidate understanding of topics presented in each chapterThis book is recommended for anyone interested in federated learning‑based intelligent algorithms for smart communications.

The effectiveness of federated learning in high‑performance information systems and informatics‑based solutions for addressing current information support requirements is demonstrated in this book. To address heterogeneity challenges in Internet of Things (IoT) contexts, Federated Learning for Smart Communication using IoT Application analyses the development of personalized federated learning algorithms capable of mitigating the detrimental consequences of heterogeneity in several dimensions. It includes case studies of IoT‑based human activity recognition to show the efficacy of personalized federated learning for intelligent IoT applications.Features:Demonstrates how federated learning offers a novel approach to building personalized models from data without invading users’ privacyDescribes how federated learning may assist in understanding and learning from user behavior in IoT applications while safeguarding user privacyPresents a detailed analysis of current research on federated learning, providing the reader with a broad understanding of the areaAnalyses the need for a personalized federated learning framework in cloud‑edge and wireless‑edge architecture for intelligent IoT applicationsComprises real‑life case illustrations and examples to help consolidate understanding of topics presented in each chapterThis book is recommended for anyone interested in federated learning‑based intelligent algorithms for smart communications.

The book presents the latest developments in intelligent communication networks based on applicability from various domains of artificial intelligence and machine learning including channel modeling, model-based structure, channel prediction, and signal detection. It further explains important topics such as vehicular mobility modeling, human-centric network applications, security and privacy in social networks, and trust-based intelligent transportation systems. This book:Presents a model-based approach to constructing an effective network by using state-of-the-art artificial intelligent techniques. Discusses the theoretical and practical applications of channel prediction and signal detection. Introduces the fundamental concepts and application of vehicular networks in conjunction with artificial intelligence. Explores wireless communication network techniques enabled by human-centric applications, designed, and developed with artificial intelligence characteristics. Highlights the challenges in designing and developing an effective and intelligent communication network that can be applied in different domains of human activities for finding sustainable solutions.It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer engineering, and information technology.

The effectiveness of federated learning in high‑performance information systems and informatics‑based solutions for addressing current information support requirements is demonstrated in this book. To address heterogeneity challenges in Internet of Things (IoT) contexts, Federated Learning for Smart Communication using IoT Application analyses the development of personalized federated learning algorithms capable of mitigating the detrimental consequences of heterogeneity in several dimensions. It includes case studies of IoT‑based human activity recognition to show the efficacy of personalized federated learning for intelligent IoT applications.

Features:

Demonstrates how federated learning offers a novel approach to building personalized models from data without invading users’ privacy Describes how federated learning may assist in understanding and learning from user behavior in IoT applications while safeguarding user privacy Presents a detailed analysis of current research on federated learning, providing the reader with a broad understanding of the area Analyses the need for a personalized federated learning framework in cloud‑edge and wireless‑edge architecture for intelligent IoT applications Comprises real‑life case illustrations and examples to help consolidate understanding of topics presented in each chapter

This book is recommended for anyone interested in federated learning‑based intelligent algorithms for smart communications.

The effectiveness of federated learning in high‑performance information systems and informatics‑based solutions for addressing current information support requirements is demonstrated in this book. To address heterogeneity challenges in Internet of Things (IoT) contexts, Federated Learning for Smart Communication using IoT Application analyses the development of personalized federated learning algorithms capable of mitigating the detrimental consequences of heterogeneity in several dimensions. It includes case studies of IoT‑based human activity recognition to show the efficacy of personalized federated learning for intelligent IoT applications.

Features:

Demonstrates how federated learning offers a novel approach to building personalized models from data without invading users’ privacy Describes how federated learning may assist in understanding and learning from user behavior in IoT applications while safeguarding user privacy Presents a detailed analysis of current research on federated learning, providing the reader with a broad understanding of the area Analyses the need for a personalized federated learning framework in cloud‑edge and wireless‑edge architecture for intelligent IoT applications Comprises real‑life case illustrations and examples to help consolidate understanding of topics presented in each chapter

This book is recommended for anyone interested in federated learning‑based intelligent algorithms for smart communications.

The book presents the latest developments in intelligent communication networks based on applicability from various domains of artificial intelligence and machine learning including channel modeling, model-based structure, channel prediction, and signal detection. It further explains important topics such as vehicular mobility modeling, human-centric network applications, security and privacy in social networks, and trust-based intelligent transportation systems.

This book:

Presents a model-based approach to constructing an effective network by using state-of-the-art artificial intelligent techniques. Discusses the theoretical and practical applications of channel prediction and signal detection. Introduces the fundamental concepts and application of vehicular networks in conjunction with artificial intelligence. Explores wireless communication network techniques enabled by human-centric applications, designed, and developed with artificial intelligence characteristics. Highlights the challenges in designing and developing an effective and intelligent communication network that can be applied in different domains of human activities for finding sustainable solutions.

It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer engineering, and information technology.