Articles liés à Federated Learning: Privacy and Incentive
Synopsis
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, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
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- ÉditeurSpringer
- Date d'édition2020
- ISBN 10 3030630757
- ISBN 13 9783030630751
- ReliureBroché
- Langueanglais
- Numéro d'édition1
- Nombre de pages296
- ÉditeurYang Qiang, Fan Lixin, Yu Han
- Coordonnées du fabricantnon disponible
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Federated Learning : Privacy and Incentive
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Federated Learning
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Springer International Publishing, 2020
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Etat : 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 introduction to Fe. N° de réf. du vendeur 417775895
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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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Springer International Publishing, Springer Nature Switzerland, 2020
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Taschenbuch. Etat : 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.'. N° de réf. du vendeur 9783030630751
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Taschenbuch. Etat : 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 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.' 296 pp. Englisch. N° de réf. du vendeur 9783030630751
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Federated Learning : Privacy and Incentive
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ISBN 10 : 3030630757
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Taschenbuch. Etat : Neu. 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 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, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.¿Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 296 pp. Englisch. N° de réf. du vendeur 9783030630751
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Federated Learning : Privacy and Incentive
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ISBN 10 : 3030630757
ISBN 13 : 9783030630751
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Federated Learning: Privacy and Incentive (Lecture Notes in Artificial Intelligence)
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