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Ajouter au panierNo jacket. Etat : Bon. Ancien livre de bibliothèque avec équipements. Sans jaquette. Edition 1966. Editeur différent. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. No dust jacket. Edition 1966. Different publisher. Ammareal gives back up to 15% of this item's net price to charity organizations.
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Edité par Springer Nature Switzerland AG, 2022
ISBN 10 : 3030855589 ISBN 13 : 9783030855581
Langue: anglais
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Edité par Springer Nature Switzerland AG, 2022
ISBN 10 : 3030855589 ISBN 13 : 9783030855581
Langue: anglais
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Edité par Springer Nature Switzerland AG, Cham, 2023
ISBN 10 : 3030855619 ISBN 13 : 9783030855611
Langue: anglais
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Ajouter au panierPaperback. Etat : new. Paperback. 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. This book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Edité par Springer Nature Switzerland AG, Cham, 2022
ISBN 10 : 3030855589 ISBN 13 : 9783030855581
Langue: anglais
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Ajouter au panierHardcover. Etat : new. Hardcover. 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. This book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Ajouter au panierEtat : New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
Edité par Springer Nature Switzerland AG, CH, 2022
ISBN 10 : 3030855589 ISBN 13 : 9783030855581
Langue: anglais
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-Uni
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Ajouter au panierHardback. Etat : New. 2022 ed. 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.
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Edité par Springer, Berlin|Springer International Publishing|Springer, 2021
ISBN 10 : 3030855589 ISBN 13 : 9783030855581
Langue: anglais
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Ajouter au panierEtat : New. This book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy.This book presents how federated learning helps to understand and learn from user activity .
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Ajouter au panierHardback. Etat : New. New copy - Usually dispatched within 4 working days. 725.
Edité par Springer Nature Switzerland, Springer International Publishing Feb 2022, 2022
ISBN 10 : 3030855589 ISBN 13 : 9783030855581
Langue: anglais
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Ajouter au panierBuch. Etat : Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 276 pp. Englisch.
Edité par Springer International Publishing, Springer International Publishing, 2022
ISBN 10 : 3030855589 ISBN 13 : 9783030855581
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 128,39
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Ajouter au panierBuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
Edité par Springer International Publishing, Springer International Publishing, 2023
ISBN 10 : 3030855619 ISBN 13 : 9783030855611
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 128,39
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
EUR 184,95
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