9783030630751 - federated learning: privacy and incentive: 12500 (17 risultati)
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Lingua: Inglese
Editore: Springer 2020-11-26, 2020
Serie: Libro 1 di 5 - Lecture Notes in Computer Science, 500
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Da: preigu, Osnabrück, Germaniapreigu
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Taschenbuch. Condizione: Neu. Federated Learning | Privacy and Incentive | Qiang Yang (u. a.) | Taschenbuch | Lecture Notes in Computer Science | 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.
Lingua: Inglese
Editore: Springer, Springer, 2020
Serie: Libro 1 di 5 - Lecture Notes in Computer Science, 500
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. 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.'.
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer, Springer Nov 2020, 2020
Serie: Libro 1 di 5 - Lecture Notes in Computer Science, 500
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 tim…ely 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.' 296 pp. Englisch.
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Condizione: New. Print on Demand.
Lingua: Inglese
Editore: Springer International Publishing, 2020
Serie: Libro 1 di 5 - Lecture Notes in Computer Science, 500
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Da: moluna, Greven, Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides a comprehensive and self-contained introduction to Federated LearningPopular topic for GDPRCovers learning, implementation and practice of Federated LearningProvides a comprehensive and self-contained introd…uction to Fe.
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Lingua: Inglese
Editore: Springer, Springer Nov 2020, 2020
Serie: Libro 1 di 5 - Lecture Notes in Computer Science, 500
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 a…s 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, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.'Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 296 pp. Englisch.
