Overview
- Provides a comprehensive and self-contained introduction to Federated Learning
- Popular topic for GDPR
- Covers learning, implementation and practice of Federated Learning
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12500)
Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)
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Table of contents (19 chapters)
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Front Matter
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Privacy
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Front Matter
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Incentive
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Front Matter
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Applications
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Front Matter
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Keywords
About this book
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications.
Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR.
This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
Editors and Affiliations
Bibliographic Information
Book Title: Federated Learning
Book Subtitle: Privacy and Incentive
Editors: Qiang Yang, Lixin Fan, Han Yu
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-030-63076-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Softcover ISBN: 978-3-030-63075-1Published: 26 November 2020
eBook ISBN: 978-3-030-63076-8Published: 25 November 2020
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: X, 286
Number of Illustrations: 12 b/w illustrations, 82 illustrations in colour
Topics: Artificial Intelligence, Systems and Data Security, Computer Communication Networks, Computer Applications