Téléchargez le livre : Federated Learning for IoT Applications

Federated Learning for IoT Applications

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Éditeur :

Springer


Collection :

EAI/Springer Innovations in Communication and Computing

Paru le : 2022-02-02

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Description
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 federatedlearning 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. 
Pages
265 pages
Collection
EAI/Springer Innovations in Communication and Computing
Parution
2022-02-02
Marque
Springer
EAN papier
9783030855581
EAN PDF
9783030855598
Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
26
Taille du fichier
7864 Ko
Prix
116,04 €
EAN EPUB
9783030855598
Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
26
Taille du fichier
27064 Ko
Prix
116,04 €

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