Federated Learning. Yang LiuЧитать онлайн книгу.

A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence

Nikos Vlassis

2007

Intelligent Autonomous Robotics: A Robot Soccer Case Study

Peter Stone

2007

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

1.2 Federated Learning as a Solution

1.2.1 The Definition of Federated Learning

1.2.2 Categories of Federated Learning

1.3 Current Development in Federated Learning

1.3.1 Research Issues in Federated Learning

1.3.2 Open-Source Projects

1.3.3 Standardization Efforts