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Federated and Transfer Learning
Published by Springer International Publishing, 2023
ISBN 10: 3031117506 ISBN 13: 9783031117503
Language: English
£ 50.49
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Add to basketCondition: Hervorragend. Zustand: Hervorragend | Seiten: 380 | Sprache: Englisch | Produktart: Bücher.
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Federated Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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£ 61.22
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Federated Learning: Privacy and Incentive (Lecture Notes in Artificial Intelligence)
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Federated Learning : Privacy and Incentive
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Federated Learning
Published by Springer International Publishing, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Language: English
£ 60.09
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Add to basketTaschenbuch. Condition: 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.
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Federated Learning : Privacy and Incentive
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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£ 72.35
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Federated Learning
Published by Springer Nature Switzerland, Springer International Publishing Dez 2019, 2019
ISBN 10: 3031004574 ISBN 13: 9783031004575
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
£ 55.47
Convert currency£ 29.36 shipping from Germany to United KingdomQuantity: 2 available
Add to basketTaschenbuch. Condition: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 208 pp. Englisch.
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Federated Learning : Privacy and Incentive
Published by Springer International Publishing, Springer Nature Switzerland, 2020
ISBN 10: 3030630757 ISBN 13: 9783030630751
Language: English
£ 73.96
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Add to basketTaschenbuch. 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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Advances and Open Problems in Federated Learning (Foundations and Trends(r) in Machine Learning)
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Federated Learning
Published by Springer International Publishing, Springer Nature Switzerland Nov 2020, 2020
ISBN 10: 3030630757 ISBN 13: 9783030630751
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
£ 73.96
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Add to basketTaschenbuch. Condition: 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.
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£ 97.51
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Add to basketCondition: New. 1st ed. 2020 edition NO-PA16APR2015-KAP.
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Federated Learning: Privacy and Incentive
Published by Springer-Nature New York Inc, 2020
ISBN 10: 3030630757 ISBN 13: 9783030630751
Language: English
Paperback. Condition: Brand New. 286 pages. 9.25x6.10x0.55 inches. In Stock.
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Federated and Transfer Learning
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Federated and Transfer Learning
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Federated and Transfer Learning (Adaptation, Learning, and Optimization, 27)
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Federated and Transfer Learning (Adaptation, Learning, and Optimization)
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Federated Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Published by Morgan & Claypool Publishers, 2019
ISBN 10: 1681736977 ISBN 13: 9781681736976
Language: English
£ 57.90
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Add to basketpaperback. Condition: 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!
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Federated and Transfer Learning
Published by Springer International Publishing, Springer International Publishing, 2023
ISBN 10: 3031117506 ISBN 13: 9783031117503
Language: English
£ 138.68
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Add to basketTaschenbuch. Condition: 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.
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Federated and Transfer Learning
Published by Springer International Publishing, 2022
ISBN 10: 3031117476 ISBN 13: 9783031117473
Language: English
£ 138.68
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Add to basketBuch. Condition: 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.
