Federated Learning

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Morgan & Claypool Publishers, 2019 M12 19 - 207 pages
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This book shows how federated machine learning allows 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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Contents

Introduction
1
Background
17
Distributed Machine Learning
33
Horizontal Federated Learning
49
Vertical Federated Learning
69
Federated Transfer Learning
83
Incentive Mechanism Design for Federated Learning
95
Federated Learning for Vision Language and Recommendation
107
Federated Reinforcement Learning
121
Selected Applications
133
Summary and Outlook
143
Legal Development on Data Protection
145
Bibliography
154
Authors Biographies
187
Copyright

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About the author (2019)

WeBank and Hong Kong University of Science and Technology, China

WeBank, China

WeBank, China

WeBank, China

WeBank, China

Nanyang Technological University, Singapore

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