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The effectiveness of federated learning in highperformance information systems and informaticsbased 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 IoTbased 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 cloudedge and wirelessedge architecture for intelligent IoT applications.
Comprises reallife case illustrations and examples to help consolidate understanding of topics presented in each chapter.
This book is recommended for anyone interested in federated learningbased intelligent algorithms for smart communications.
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