Federated Learning. Yang Liu

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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

       Preface

       Acknowledgments

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