Kishor K., Nand P., Jain V., Saxena N., Agarwal G., Astya R. (Eds.) Federated Learning for Smart Communication using IoT Application

CRC Press, 2025. — 275 p. — (Chapman & Hall/CRC Cyber‑Physical Systems). — ISBN: 978‑1‑032‑78813‑5.The effectiveness of Federated Learning (FL) 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 analyzes 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.Federated Learning (FL) is leading the way in revolutionary developments in Machine Learning, transforming the traditional field of centralized model training. Fundamentally, FL is a novel technique that enables a network of dispersed devices to jointly train Machine Learning models. FL prioritizes privacy above central processing of raw data, as is the case with traditional approaches. Individual devices — such as cellphones, edge devices, or other endpoints — contribute to model training under this novel paradigm without disclosing private information. We will explore the fundamentals of FL, its uses, and its potential to revolutionize the ever-evolving field of Artificial Intelligence (AI) as we delve into its depths.FeaturesDemonstrates 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.
Analyzes 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.Introduction to Federated Learning: Transforming Collaborative Machine Learning for a Decentralized Future.
Applications, Challenges, and Opportunities for Federated Learning in 6G.
Unleash Federated Machine Learning and the Internet of Medical Things (IoMT) for Disease Screening and Enhancement of Smart Healthcare.
Federated Machine Learning in Medical Science: A Perspective Investigation.
Artificial Intelligence Techniques Based on Federated Learning in Smart Healthcare.
Federated Machine Learning in Medical Science: A Prospective Investigation.
Healthcare Informatics Security Issues and Solutions Using Federated Learning.
Innovative Solutions: Exploring Federated Learning‑Based Resource Virtualization with AR Integration in Healthcare Environments.
Securing the Connected World: Federated Learning and IoT Cybersecurity.
Federated Learning Shaping the Future of Smart City Infrastructure.
Empowering Teaching Institutes: Integrating Federated Learning in the Internet of Things (IoT).
A Critical Role for Federated Learning in IoT.