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Federated Learning Privacy Incentive (14 results)
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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)
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Federated Learning : Privacy and Incentive
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Federated Learning : Privacy and Incentive
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Condition: New. 1st ed. 2020 edition NO-PA16APR2015-KAP.
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Federated Learning: Privacy and Incentive
Language: English
Published by Springer-Nature New York Inc, 2020
ISBN 10: 3030630757 ISBN 13: 9783030630751
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Add to basketPaperback. Condition: Brand New. 286 pages. 9.25x6.10x0.55 inches. In Stock.
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Federated Learning : Privacy and Incentive
Language: English
Published by Springer International Publishing, Springer Nature Switzerland, 2020
ISBN 10: 3030630757 ISBN 13: 9783030630751
Taschenbuch. Condition: 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.'.
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Taschenbuch. Condition: Neu. Federated Learning | Privacy and Incentive | Qiang Yang (u. a.) | Taschenbuch | x | Englisch | 2020 | Springer | EAN 9783030630751 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
