LINGUIST List 31.325

Thu Jan 23 2020

Books: Federated Learning: Yang, Liu, Cheng, Kang

Editor for this issue: Jeremy Coburn <>

Date: 22-Jan-2020
From: Bebe Barrow <>
Subject: Federated Learning: Yang, Liu, Cheng, Kang
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Title: Federated Learning
Series Title: Synthesis Lectures on Artificial Intelligence and Machine Learning edited by Ronald Brachman, Francesca Rossi, and Peter Stone
Published: 2020
Publisher: Morgan & Claypool Publishers

Book URL:

Author: Qiang Yang
Author: Yang Liu
Author: Yong Cheng
Author: Yan Kang
Electronic: ISBN: 9781681736983 Pages: 207 Price: U.S. $ 63.96
Hardback: ISBN: 9781681736990 Pages: 207 Price: U.S. $ 99.95
Paperback: ISBN: 9781681736976 Pages: 207 Price: U.S. $ 79.95

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.

Linguistic Field(s): Cognitive Science
                            Computational Linguistics

Written In: English (eng)

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Page Updated: 23-Jan-2020