Federated Learning for Smart Communication using IoT Application (Chapman & Hall/CRC Cyber-Physical Systems)

Federated Learning for Smart Communication using IoT Application (Chapman & Hall/CRC Cyber-Physical Systems)
by: Kaushal Kishor (Editor),Parma Nand (Editor),Vishal Jain (Editor),Neetesh Saxena (Editor),Gaurav Agarwal (Editor),Rani Astya (Editor)
Publisher: Chapman and Hall/CRC
Edition: 1st
Publication Date: 2024/10/30
Language: English
Print Length: 260 pages
ISBN-10: 1032788127
ISBN-13: 9781032788128
Book Description
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.
About the Author
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. Read more