Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 75,11
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Editore: Springer International Publishing, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 69,54
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
EUR 69,08
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Editore: Springer International Publishing, 2023
ISBN 10: 3031117506 ISBN 13: 9783031117503
Lingua: Inglese
Da: Buchpark, Trebbin, Germania
EUR 58,43
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Aggiungi al carrelloCondizione: Hervorragend. Zustand: Hervorragend | Seiten: 380 | Sprache: Englisch | Produktart: Bücher.
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 85,72
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 81,49
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EUR 69,08
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EUR 81,48
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Editore: Springer International Publishing, Springer Nature Switzerland, 2020
ISBN 10: 3030630757 ISBN 13: 9783030630751
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 85,59
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR.This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, andneural network. Additionally, domain knowledge in FinTech and marketing would be helpful.'.
EUR 85,72
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: California Books, Miami, FL, U.S.A.
EUR 96,04
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EUR 86,94
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Da: California Books, Miami, FL, U.S.A.
EUR 97,85
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EUR 88,79
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EUR 96,94
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 106,86
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Aggiungi al carrelloCondizione: New. In.
Da: Books Puddle, New York, NY, U.S.A.
EUR 115,91
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Aggiungi al carrelloCondizione: New. 1st ed. 2020 edition NO-PA16APR2015-KAP.
Editore: Springer-Nature New York Inc, 2020
ISBN 10: 3030630757 ISBN 13: 9783030630751
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
EUR 129,41
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Aggiungi al carrelloPaperback. Condizione: Brand New. 286 pages. 9.25x6.10x0.55 inches. In Stock.
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 86,75
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EUR 149,27
Convertire valutaQuantità: 4 disponibili
Aggiungi al carrelloCondizione: New. pp. 226.
Da: dsmbooks, Liverpool, Regno Unito
EUR 132,74
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Aggiungi al carrelloPaperback. Condizione: New. New. book.
Editore: Morgan & Claypool Publishers, 2019
ISBN 10: 1681736977 ISBN 13: 9781681736976
Lingua: Inglese
Da: HPB-Red, Dallas, TX, U.S.A.
EUR 68,82
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Aggiungi al carrellopaperback. Condizione: 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!
Editore: Springer International Publishing, Springer International Publishing, 2023
ISBN 10: 3031117506 ISBN 13: 9783031117503
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 160,49
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.
Editore: Springer International Publishing, 2022
ISBN 10: 3031117476 ISBN 13: 9783031117473
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 160,49
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Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 170,81
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 170,81
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EUR 163,36
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EUR 168,05
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EUR 170,80
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Da: California Books, Miami, FL, U.S.A.
EUR 183,02
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Aggiungi al carrelloCondizione: New.