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Da: Best Price, Torrance, CA, U.S.A.
EUR 61,32
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 65,90
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Da: California Books, Miami, FL, U.S.A.
EUR 74,63
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EUR 58,40
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 63,27
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Editore: Springer International Publishing AG, CH, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Lingua: Inglese
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 81,19
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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 67,12
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Da: Books Puddle, New York, NY, U.S.A.
EUR 86,15
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Da: Revaluation Books, Exeter, Regno Unito
EUR 73,49
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Aggiungi al carrelloPaperback. Condizione: Brand New. 9.25x7.51 inches. In Stock.
Editore: Springer International Publishing AG, CH, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Lingua: Inglese
Da: Rarewaves.com UK, London, Regno Unito
EUR 75,02
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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.
Da: Majestic Books, Hounslow, Regno Unito
EUR 88,51
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 91,34
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Editore: Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 60,06
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Aggiungi al carrelloCondizione: 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, .