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Federated Learning by Yang Qiang (75 results)
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Federated Learning
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Condition: New.
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Condition: As New. Unread book in perfect condition.
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Federated Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Published by Morgan & Claypool Publishers, 2019
ISBN 10: 1681736977 ISBN 13: 9781681736976
Language: English
paperback. Condition: 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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Federated Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In English.
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Federated Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Federated Learning
Published by Springer International Publishing AG, CH, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: 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.
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Federated Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Federated Learning
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
Condition: New.
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Condition: As New. Unread book in perfect condition.
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Federated Learning: Privacy and Incentive (Lecture Notes in Artificial Intelligence)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Condition: New. 1st edition NO-PA16APR2015-KAP.
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Federated Learning : Privacy and Incentive
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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PF. Condition: New.
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Condition: NEW.
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Federated Learning : Privacy and Incentive
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Condition: New. 1st ed. 2020 edition NO-PA16APR2015-KAP.
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Federated Learning
Published by Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Language: English
Condition: 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, .
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Federated Learning
Published by Springer International Publishing, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Language: English
Taschenbuch. Condition: 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.
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Taschenbuch. Condition: Neu. Federated Learning | Qiang Yang (u. a.) | Taschenbuch | xvii | Englisch | 2019 | Springer | EAN 9783031004575 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Condition: New. pp. 226.
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Federated Learning: Privacy and Incentive
Published by Springer-Nature New York Inc, 2020
ISBN 10: 3030630757 ISBN 13: 9783030630751
Language: English
Paperback. Condition: Brand New. 286 pages. 9.25x6.10x0.55 inches. In Stock.
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Federated Learning
Published by Springer International Publishing AG, CH, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Language: English
Paperback. Condition: 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.
