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Condizione: As New. Unread book in perfect condition.
Condizione: New.
Editore: Morgan & Claypool Publishers, 2019
ISBN 10: 1681736977 ISBN 13: 9781681736976
Lingua: Inglese
Da: HPB-Red, Dallas, TX, U.S.A.
paperback. 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!
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 61,21
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Aggiungi al carrelloCondizione: New. In English.
Editore: Springer International Publishing AG, CH, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Lingua: Inglese
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 78,63
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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 64,87
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. 1st 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
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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, .
Editore: Springer International Publishing AG, CH, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Lingua: Inglese
Da: Rarewaves.com UK, London, Regno Unito
EUR 70,68
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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 110,00
Quantità: 5 disponibili
Aggiungi al carrellopaperback. Condizione: 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 .
Da: Revaluation Books, Exeter, Regno Unito
EUR 70,96
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 206 pages. 9.25x7.51x9.25 inches. In Stock. This item is printed on demand.
Da: Majestic Books, Hounslow, Regno Unito
EUR 91,69
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Aggiungi al carrelloCondizione: New. Print on Demand.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 93,06
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