Verlag: Electronic Industry Press, 2020
ISBN 10: 7121385228 ISBN 13: 9787121385223
Sprache: Chinesisch
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In den WarenkorbZustand: New. In English.
Verlag: Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Sprache: Englisch
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In den WarenkorbZustand: 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, .
Verlag: Springer International Publishing AG, CH, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: 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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In den WarenkorbZustand: New.
Verlag: Springer International Publishing AG, CH, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: 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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In den WarenkorbZustand: New.
Verlag: Morgan & Claypool Publishers, 2019
ISBN 10: 1681736977 ISBN 13: 9781681736976
Sprache: Englisch
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Verlag: Electronic Industry Press, 2020
ISBN 10: 7121385228 ISBN 13: 9787121385223
Sprache: Chinesisch
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In den Warenkorbpaperback. Zustand: 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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