You might also like

More about Federated Learning

• Book details
• Synopsis
• Trending among others

You get 159 loyalty points

Book synopsis

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.

Book details

Book category Books in German Naturwissenschaften, Medizin, Informatik, Technik Informatik, EDV Informatik

• Full title: Federated Learning
• Author: Yang Yang Liu, Han Han Yu, Yan Yan Kang, Tianjian Tianjian Chen, Yong Yong Cheng
• Language: English
• Binding: Paperback
• Number of pages: 189
• EAN: 9783031004575
• ISBN: 3031004574
• ID: 39298077
• Publisher: Springer International Publishing AG
• Weight: 401 g
• Dimensions: 235 × 191 × 12 mm
• Date of publishing: 19. December 2019

Trending among others

---

---

---