Descrizione libro Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application. 208 pp. Englisch. Codice articolo 9783031004575

Informazioni sul venditore | Contatta il venditore

Compra nuovo

EUR 69,54

Convertire valuta

Aggiungere al carrello

Spese di spedizione: EUR 23,00

Da: Germania a: U.S.A.

Destinazione, tempi e costi

Foto dell'editore

Federated Learning

Yang, Qiang

Editore: Springer (2019)

ISBN 10: 3031004574 ISBN 13: 9783031004575

Nuovo Brossura Quantit�: 15

Da:

Kennys Bookshop and Art Galleries Ltd.
(Galway, GY, Irlanda)

Valutazione libreria

Valutazione venditore:

Descrizione libro Condizione: New. Codice articolo V9783031004575

Informazioni sul venditore | Contatta il venditore

Compra nuovo

EUR 90,84

Convertire valuta

Aggiungere al carrello

Spese di spedizione: EUR 10,50

Da: Irlanda a: U.S.A.

Destinazione, tempi e costi

Immagini fornite dal venditore

Federated Learning

Qiang Yang

Editore: Springer International Publishing (2019)

ISBN 10: 3031004574 ISBN 13: 9783031004575

Nuovo Taschenbuch Quantit�: 1

Da:

AHA-BUCH GmbH
(Einbeck, Germania)

Valutazione libreria

Valutazione venditore:

Descrizione libro Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application. Codice articolo 9783031004575

Informazioni sul venditore | Contatta il venditore

Compra nuovo

EUR 71,42

Convertire valuta

Aggiungere al carrello

Spese di spedizione: EUR 32,99

Da: Germania a: U.S.A.

Destinazione, tempi e costi