Federated Learning for IoT Applications

This book explores how federated learning can be used to understand and learn from user activity in IoT applications while protecting user privacy. It provides a comprehensive survey of state-of-the-art research and investigates how a personalized federated learning framework is needed in cloud-edge and wireless-edge architecture. Case studies of IoT-based human activity recognition demonstrate the effectiveness of personalized federated learning for intelligent IoT applications.

Format: Paperback / softback
Length: 265 pages
Publication date: 04 February 2023
Publisher: Springer Nature Switzerland AG

This book delves into the realm of federated learning, a groundbreaking approach that enables the understanding and learning from user activity in Internet of Things (IoT) applications while simultaneously safeguarding user privacy. The authors commence by showcasing how federated learning offers a distinctive method to create personalized models utilizing data without compromising users' privacy. Subsequently, they conduct a comprehensive survey of state-of-the-art research in federated learning, providing the reader with a comprehensive overview of the field. Furthermore, the book explores the necessity of a personalized federated learning framework in cloud-edge and wireless-edge architectures for intelligent IoT applications. To address the heterogeneity challenges prevalent in IoT environments, the book investigates emerging personalized federated learning methods that effectively mitigate the negative effects caused by such heterogeneities in various aspects. Additionally, the book presents case studies of IoT-based human activity recognition to exemplify the efficacy of personalized federated learning in intelligent IoT applications. Furthermore, it offers a suite of multiple controller design and system analysis tools, including model predictive control, linear matrix inequalities, optimal control, and more. This comprehensive and unique co-design framework holds immense benefits for researchers, graduate students, and engineers specializing in control theory and engineering.

Weight: 427g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030855611
Edition number: 1st ed. 2022