Federated Learning. Yang LiuЧитать онлайн книгу.

       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

       1 Introduction

       1.1 Motivation

       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

       1.3.4 The Federated AI Ecosystem

       1.4 Organization of this Book

       2 Background

       2.1 Privacy-Preserving Machine Learning

       2.2 PPML and Secure ML

       2.3 Threat and Security Models

       2.3.1 Privacy Threat Models

       2.3.2 Adversary and Security Models

       2.4 Privacy Preservation Techniques

       2.4.1 Secure Multi-Party Computation

       2.4.2 Homomorphic Encryption

       2.4.3 Differential Privacy

       3 Distributed Machine Learning

       3.1 Introduction to DML

       3.1.1