Edité par Electronic Industry Press, 2020
ISBN 10 : 7121385228 ISBN 13 : 9787121385223
Langue: chinois
Vendeur : ThriftBooks-Dallas, Dallas, TX, Etats-Unis
EUR 8,78
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Ajouter au panierPaperback. Etat : Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 1.01.
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 63,24
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Ajouter au panierEtat : New. In English.
EUR 58,37
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Edité par Springer International Publishing AG, CH, 2019
ISBN 10 : 3031004574 ISBN 13 : 9783031004575
Langue: anglais
Vendeur : Rarewaves.com UK, London, Royaume-Uni
EUR 74,98
Autre deviseQuantité disponible : 1 disponible(s)
Ajouter au panierPaperback. Etat : 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 62,41
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Vendeur : California Books, Miami, FL, Etats-Unis
EUR 74,81
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EUR 64,76
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Ajouter au panierEtat : As New. Unread book in perfect condition.
Edité par Springer International Publishing AG, CH, 2019
ISBN 10 : 3031004574 ISBN 13 : 9783031004575
Langue: anglais
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-Uni
EUR 81,38
Autre deviseQuantité disponible : 1 disponible(s)
Ajouter au panierPaperback. Etat : 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 67,08
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Ajouter au panierEtat : As New. Unread book in perfect condition.
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 73,46
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Ajouter au panierPaperback. Etat : Brand New. 9.25x7.51 inches. In Stock.
Vendeur : Books Puddle, New York, NY, Etats-Unis
EUR 86,35
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Ajouter au panierEtat : New. 1st edition NO-PA16APR2015-KAP.
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
EUR 66,06
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Ajouter au panierEtat : New.
Edité par Morgan & Claypool Publishers, 2019
ISBN 10 : 1681736977 ISBN 13 : 9781681736976
Langue: anglais
Vendeur : HPB-Red, Dallas, TX, Etats-Unis
EUR 66,85
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Ajouter au panierpaperback. Etat : 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!
Edité par Electronic Industry Press, 2020
ISBN 10 : 7121385228 ISBN 13 : 9787121385223
Langue: chinois
Vendeur : liu xing, Nanjing, JS, Chine
EUR 108,76
Autre deviseQuantité disponible : 5 disponible(s)
Ajouter au panierpaperback. Etat : New. Paperback. Pub Date: 2020-04-01 Language: Chinese Publisher: How to implement multiple data owners cooperate training a shared machine learning model with multiple data owners in the premise of ensuring that local training data is not disclosed? Traditional machine learning methods need to concentrate all data to a place (for example. data center). then make machine learning mode .
Edité par Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2019
ISBN 10 : 3031004574 ISBN 13 : 9783031004575
Langue: anglais
Vendeur : moluna, Greven, Allemagne
EUR 60,06
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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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, .
Vendeur : Majestic Books, Hounslow, Royaume-Uni
EUR 88,72
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Ajouter au panierEtat : New. Print on Demand.
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
EUR 91,34
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Ajouter au panierEtat : New. PRINT ON DEMAND.