Federated Learning by Yang Qiang (70 results)
Language: English
Published by Springer, 2019
Series: Book 13 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Language: English
Published by Morgan & Claypool, 2019
Series: Book 13 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Language: English
Published by Springer, 2019
Series: Book 13 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Language: English
Published by Springer, 2020
Series: Book 1 of 5 - Lecture Notes in Computer Science, 500
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Language: English
Published by Springer, 2020
Series: Book 1 of 5 - Lecture Notes in Computer Science, 500
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Federated Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Yang, Qiang; Liu, Yang; Cheng, Yong; Kang, Yan; Chen, Tianjian; Yu, Han
Language: English
Published by Springer, 2019
Series: Book 13 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Condition: New. In English.
Language: English
Published by Springer 2019-12, 2019
Series: Book 13 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Language: English
Published by Springer, 2019
Series: Book 13 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Language: English
Published by Springer 2020-11, 2020
Series: Book 1 of 5 - Lecture Notes in Computer Science, 500
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Federated Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Yang, Qiang; Liu, Yang; Cheng, Yong; Kang, Yan; Chen, Tianjian; Yu, Han
Language: English
Published by Springer, 2019
Series: Book 13 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Condition: New. 1st edition NO-PA16APR2015-KAP.
Language: English
Published by Springer, 2020
Series: Book 1 of 5 - Lecture Notes in Computer Science, 500
- Softcover
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Language: English
Published by Springer, 2019
Series: Book 13 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Language: English
Published by Springer, 2020
Series: Book 1 of 5 - Lecture Notes in Computer Science, 500
- Softcover
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Language: English
Published by Springer, 2019
Series: Book 13 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Language: English
Published by Springer, 2020
Series: Book 1 of 5 - Lecture Notes in Computer Science, 500
- Softcover
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Language: English
Published by Springer, 2019
Series: Book 13 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Language: English
Published by Springer, 2020
Series: Book 1 of 5 - Lecture Notes in Computer Science, 500
- Softcover
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Condition: New. 1st ed. 2020 edition NO-PA16APR2015-KAP.
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Language: English
Published by Springer International Publishing, 2019
Series: Book 13 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Taschenbuch. 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, typicall…y 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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Language: English
Published by Springer, 2019
Series: Book 13 of 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Taschenbuch. Condition: Neu. Federated Learning | Qiang Yang (u. a.) | Taschenbuch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xvii | Englisch | 2019 | Springer | EAN 9783031004575 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]sp…ringer[dot]com | Anbieter: preigu.
Language: English
Published by Springer-Nature New York Inc, 2020
Series: Book 1 of 5 - Lecture Notes in Computer Science, 500
- Softcover
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Paperback. Condition: Brand New. 286 pages. 9.25x6.10x0.55 inches. In Stock.
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Language: English
Published by Springer, 2020
Series: Book 1 of 5 - Lecture Notes in Computer Science, 500
- Softcover
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Taschenbuch. Condition: 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]c…om | Anbieter: preigu.
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Language: English
Published by Springer, Springer, 2020
Series: Book 1 of 5 - Lecture Notes in Computer Science, 500
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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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.'.
Federated and Transfer Learning
Razavi-far, Roozbeh (EDT); Wang, Boyu (EDT); Taylor, Matthew E. (EDT); Yang, Qiang (EDT)
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Language: English
Published by Springer International Publishing AG, Cham, 2022
- Hardcover
- First Edition
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Hardcover. Condition: new. Hardcover. 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 le…arning 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Softcover
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Condition: Hervorragend. Zustand: Hervorragend | Seiten: 380 | Sprache: Englisch | Produktart: Bücher | 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 factor…s 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.
- Hardcover
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Federated and Transfer Learning
Razavi-far, Roozbeh (EDT); Wang, Boyu (EDT); Taylor, Matthew E. (EDT); Yang, Qiang (EDT)
- Hardcover
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Federated and Transfer Learning
Razavi-far, Roozbeh (EDT); Wang, Boyu (EDT); Taylor, Matthew E. (EDT); Yang, Qiang (EDT)
- Softcover
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Federated and Transfer Learning
Razavi-far, Roozbeh (EDT); Wang, Boyu (EDT); Taylor, Matthew E. (EDT); Yang, Qiang (EDT)
- Softcover
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