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Qiang Yang Liu Yong Cheng (18 results)
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Federal Learning (full color) (blog views)(Chinese Edition)
Published by Electronic Industry Press, 2020
ISBN 10: 7121385228 ISBN 13: 9787121385223
Language: Chinese
Paperback. Condition: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 1.01.
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US$ 72.53
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US$ 74.51
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Condition: New. SUPER FAST SHIPPING.
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US$ 75.06
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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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Condition: New.
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Federated Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 69.50
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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
US$ 75.29
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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
US$ 93.32
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Add to basketPaperback. 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
US$ 79.84
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Add to basketCondition: As New. Unread book in perfect condition.
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Condition: New. 1st edition NO-PA16APR2015-KAP.
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US$ 87.46
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Add to basketPaperback. Condition: Brand New. 9.25x7.51 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
US$ 89.27
Convert currencyUS$ 88.59 shipping from United Kingdom to U.S.A.Quantity: 1 available
Add to basketPaperback. 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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Federal Learning (full color) (blog views)(Chinese Edition)
Published by Electronic Industry Press, 2020
ISBN 10: 7121385228 ISBN 13: 9787121385223
Language: Chinese
US$ 123.58
Convert currencyUS$ 18.00 shipping from China to U.S.A.Quantity: 5 available
Add to basketpaperback. Condition: 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 .
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US$ 98.08
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US$ 109.14
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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
US$ 73.15
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Add to basketCondition: 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, .
