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Trading Agents
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Visual Object Recognition
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Learning with Support Vector Machines
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Data Integration: The Relational Logic Approach
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Markov Logic: An Interface Layer for Artificial Intelligence
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Introduction to Semi-Supervised Learning
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Action Programming Languages
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Representation Discovery using Harmonic Analysis
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Essentials of Game Theory: A Concise Multidisciplinary Introduction
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A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
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Copyright © 2020 by Morgan & Claypool
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher.
Federated Learning
Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu
www.morganclaypool.com
ISBN: 9781687336976 paperback
ISBN: 9781687336983 ebook
ISBN: 9781687336990 hardcover
DOI 10.2200/S00960ED2V01Y201910AIM043
A Publication in the Morgan & Claypool Publishers series
SYNTHESIS LECTURES ON ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
Lecture #43
Series Editors: Ronald J. Brachman, Jacobs Technion-Cornell Institute at Cornell Tech
Francesca Rossi, IBM Research AI
Peter Stone, University of Texas at Austin
Series ISSN
Synthesis Lectures on Artificial Intelligence and Machine Learning
Print 1939-4608 Electronic 1939-4616
Federated Learning
Qiang Yang
WeBank and Hong Kong University of Science and Technology, China
Yang Liu
WeBank, China
Yong Cheng
WeBank, China
Yan Kang
WeBank, China
Tianjian Chen
WeBank, China
Han Yu
Nanyang Technological University, Singapore
SYNTHESIS LECTURES ON ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING #43
ABSTRACT
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.
KEYWORDS
federated learning, secure multi-party computation, privacy preserving machine learning, machine learning algorithms, transfer learning, artificial intelligence, data confidentiality, GDPR, privacy regulations
Contents
