EUR 58,66
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloPAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
EUR 62,99
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
EUR 63,27
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 66,84
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Editore: Morgan & Claypool Publishers, 2019
ISBN 10: 1681736977 ISBN 13: 9781681736976
Lingua: Inglese
Da: HPB-Red, Dallas, TX, U.S.A.
EUR 67,65
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrellopaperback. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Da: California Books, Miami, FL, U.S.A.
EUR 75,70
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 61,92
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. In English.
EUR 58,65
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Editore: Springer International Publishing AG, Cham, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Lingua: Inglese
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
EUR 77,47
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: new. Paperback. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Editore: Springer International Publishing AG, CH, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Lingua: Inglese
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 80,04
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: New. 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.
EUR 65,61
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
EUR 83,47
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
EUR 85,45
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Editore: Springer Nature Switzerland AG, Cham, 2020
ISBN 10: 3030630757 ISBN 13: 9783030630751
Lingua: Inglese
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Prima edizione
EUR 87,81
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: new. Paperback. This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR.This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful. This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
EUR 68,99
Convertire valutaQuantità: 10 disponibili
Aggiungi al carrelloPF. Condizione: New.
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 85,27
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Da: Books Puddle, New York, NY, U.S.A.
EUR 86,66
Convertire valutaQuantità: 4 disponibili
Aggiungi al carrelloCondizione: New. 1st edition NO-PA16APR2015-KAP.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 78,69
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. In.
EUR 75,15
Convertire valutaQuantità: 10 disponibili
Aggiungi al carrelloPF. Condizione: New.
Da: California Books, Miami, FL, U.S.A.
EUR 96,18
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
EUR 78,10
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
EUR 53,15
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloCondizione: NEW.
EUR 85,72
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
EUR 95,48
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloCondizione: New.
Da: Books Puddle, New York, NY, U.S.A.
EUR 114,44
Convertire valutaQuantità: 4 disponibili
Aggiungi al carrelloCondizione: New. 1st ed. 2020 edition NO-PA16APR2015-KAP.
Editore: Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 67,49
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloCondizione: New. 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, .
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
Prima edizione
EUR 106,55
Convertire valutaQuantità: 15 disponibili
Aggiungi al carrelloCondizione: New. 2020. 1st ed. 2020. paperback. . . . . .
EUR 119,70
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloCondizione: New.
Editore: Springer International Publishing, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 69,54
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. 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.
EUR 138,36
Convertire valutaQuantità: 4 disponibili
Aggiungi al carrelloCondizione: New. pp. 226.