CYBER DEAL: 50% off all Springer eBooks | Get this offer!

Lecture Notes in Artificial Intelligence Lect.Notes ComputerState-of-the-Art Surveys

Federated Learning

Privacy and Incentive

Editors: Yang, Qiang, Fan, Lixin, Yu, Han (Eds.)

Free Preview
  • Provides a comprehensive and self-contained introduction to Federated Learning
    Popular topic for GDPR
    Covers learning, implementation and practice of Federated Learning

Buy this book

eBook 48,14 €
price for Spain (gross)
  • Due: December 27, 2020
  • ISBN 978-3-030-63076-8
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Softcover 60,31 €
price for Spain (gross)
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.”

 


Table of contents (19 chapters)

Table of contents (19 chapters)

Buy this book

eBook 48,14 €
price for Spain (gross)
  • Due: December 27, 2020
  • ISBN 978-3-030-63076-8
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Softcover 60,31 €
price for Spain (gross)
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
Federated Learning
Book Subtitle
Privacy and Incentive
Editors
  • Qiang Yang
  • Lixin Fan
  • Han Yu
Series Title
Lecture Notes in Artificial Intelligence
Series Volume
12500
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-63076-8
DOI
10.1007/978-3-030-63076-8
Softcover ISBN
978-3-030-63075-1
Edition Number
1
Number of Pages
X, 286
Number of Illustrations
12 b/w illustrations, 82 illustrations in colour
Topics