Publicado por Electronic Industry Press, 2020
ISBN 10: 7121385228 ISBN 13: 9787121385223
Idioma: Chino
Librería: ThriftBooks-Dallas, Dallas, TX, Estados Unidos de America
EUR 8,82
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Añadir al carritoPaperback. Condición: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 1.01.
Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 62,01
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Publicado por Springer International Publishing AG, CH, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Idioma: Inglés
Librería: Rarewaves.com UK, London, Reino Unido
EUR 73,47
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Añadir al carritoPaperback. Condición: 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 58,74
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EUR 63,07
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Añadir al carritoCondición: As New. Unread book in perfect condition.
EUR 63,19
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Publicado por Springer International Publishing AG, CH, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Idioma: Inglés
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
EUR 78,58
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Añadir al carritoPaperback. Condición: 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.
Librería: California Books, Miami, FL, Estados Unidos de America
EUR 75,11
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EUR 65,72
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Publicado por Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Idioma: Inglés
Librería: moluna, Greven, Alemania
EUR 67,49
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Añadir al carritoCondición: 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, .
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 85,99
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Añadir al carritoCondición: New. 1st edition NO-PA16APR2015-KAP.
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
EUR 66,33
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Publicado por Morgan & Claypool Publishers, 2019
ISBN 10: 1681736977 ISBN 13: 9781681736976
Idioma: Inglés
Librería: HPB-Red, Dallas, TX, Estados Unidos de America
EUR 67,12
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Añadir al carritopaperback. Condición: 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!
Publicado por Electronic Industry Press, 2020
ISBN 10: 7121385228 ISBN 13: 9787121385223
Idioma: Chino
Librería: liu xing, Nanjing, JS, China
EUR 109,20
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Añadir al carritopaperback. Condición: 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 .
Librería: Revaluation Books, Exeter, Reino Unido
EUR 71,88
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Añadir al carritoPaperback. Condición: Brand New. 206 pages. 9.25x7.51x9.25 inches. In Stock. This item is printed on demand.
Librería: Majestic Books, Hounslow, Reino Unido
EUR 88,47
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Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 90,68
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