Neu Erschienen: 04.02.2023 Abbildung von Yadav / Bhati | Federated Learning for IoT Applications | 1. Auflage | 2023 |

Yadav / Bhati / Mahato / Kumar

Federated Learning for IoT Applications

lieferbar ca. 10 Tage als Sonderdruck ohne Rückgaberecht

106,99 €

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117,69 €

Preisangaben inkl. MwSt. Abhängig von der Lieferadresse kann die MwSt. an der Kasse variieren. Weitere Informationen

Buch. Softcover

1st ed. 2022. 2023

viii, 265 S. 21 s/w-Abbildungen, 59 Farbabbildungen, Bibliographien.

In englischer Sprache

Springer. ISBN 978-3-030-85561-1

Format (B x L): 15,5 x 23,5 cm

Gewicht: 427 g


This book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy. The authors first show how federated learning provides a unique way to build personalized models using data without intruding on users’ privacy. The authors then provide a comprehensive survey of state-of-the-art research on federated learning, giving the reader a general overview of the field. The book also investigates how a personalized federated learning framework is needed in cloud-edge architecture as well as in wireless-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, the book investigates emerging personalized federated learning methods that are able to mitigate the negative effects caused by heterogeneities in different aspects. The book provides case studies of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications, as well as multiple controller design and system analysis tools including model predictive control, linear matrix inequalities, optimal control, etc. This unique and complete co-design framework will benefit researchers, graduate students and engineers in the fields of control theory and engineering. - Shows how federated learning utilizes data generated by consumer devices without intruding on privacy, allowing machine learning models to deliver personalized services; - Analyzes how federated learning provides a privacy-preserving mechanism to effectively leverage decentralized resources inside end-devices to train machine learning models; - Presents case studies that provide a tried and tested approaches to resolution of typical problems in federated learning.

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