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  2. Anvendt matematik
  3. Matematisk modellering

Federated Learning

Af: Han Yu, Qiang Yang, Yang Liu, Tianjian Chen, Yong Cheng, Yan Kang Engelsk Paperback

Federated Learning

Af: Han Yu, Qiang Yang, Yang Liu, Tianjian Chen, Yong Cheng, Yan Kang Engelsk Paperback
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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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Tjek vores konkurrenters priser

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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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 189
ISBN-13: 9783031004575
Indbinding: Paperback
Udgave:
ISBN-10: 3031004574
Kategori: Matematisk modellering
Udg. Dato: 19 dec 2019
Længde: 0mm
Bredde: 191mm
Højde: 235mm
Forlag: Springer International Publishing AG
Oplagsdato: 19 dec 2019
Forfatter(e): Han Yu, Qiang Yang, Yang Liu, Tianjian Chen, Yong Cheng, Yan Kang
Forfatter(e) Han Yu, Qiang Yang, Yang Liu, Tianjian Chen, Yong Cheng, Yan Kang

Kategori Matematisk modellering

ISBN-13 9783031004575

Sprog Engelsk

Indbinding Paperback

Sider 189

Udgave

Længde 0mm

Bredde 191mm

Højde 235mm

Udg. Dato 19 dec 2019

Oplagsdato 19 dec 2019

Forlag Springer International Publishing AG

Kategori sammenhænge