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Federated Learning, Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu


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Цена: 12335.00р.
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Автор: Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu
Название:  Federated Learning
ISBN: 9781681736976
Издательство: Mare Nostrum (Eurospan)
Классификация:



ISBN-10: 1681736977
Обложка/Формат: Paperback
Страницы: 207
Вес: 0.37 кг.
Дата издания: 30.12.2019
Серия: Synthesis lectures on artificial intelligence and machine learning
Язык: English
Размер: 235 x 191 x 11
Читательская аудитория: Professional and scholarly
Ключевые слова: Information technology: general issues,Computer science,Artificial intelligence, COMPUTERS / Computer Science,COMPUTERS / General,COMPUTERS / Intelligence (AI) & Semantics
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Поставляется из: Англии
Описание: 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.


Federated Learning

Автор: Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu
Название: Federated Learning
ISBN: 1681736993 ISBN-13(EAN): 9781681736990
Издательство: Mare Nostrum (Eurospan)
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Цена: 15523.00 р.
Наличие на складе: Нет в наличии.

Описание: 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.

Proceedings of the 2015 Federated Conference on Software Development and Object Technologies

Автор: Janech
Название: Proceedings of the 2015 Federated Conference on Software Development and Object Technologies
ISBN: 3319465341 ISBN-13(EAN): 9783319465340
Издательство: Springer
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Цена: 27950.00 р.
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Описание: This book presents the proceedings of the International Conference SDOT which was organized at the University in ?ilina, Faculty of Management Sciences and Informatics, Slovak Republic in November 19, 2015. The conference was truly international both in terms of the amount of foreign contributions and in terms of composition of steering and scientific committees. The book and the conference serves as a platform of professional exchange of knowledge and experience for the latest trends in software development and object-oriented technologies (theory and practice). This proceedings present information on the latest developments and mediate the exchange of experience between practitioners and academia.


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