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Studies in Computational Intelligence

Federated Learning Systems

Towards Next-Generation AI

Editors: Rehman, Muhammad Habib ur, Gaber, Mohamed Medhat (Eds.)

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  • Presents advances in federated learning
  • Shows how federated learning can transform next-generation artificial intelligence applications
  • Proposes solutions to address key federated learning challenges
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eBook $119.00
price for USA in USD
  • ISBN 978-3-030-70604-3
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
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  • Immediate eBook download after purchase
Hardcover $159.99
price for USA in USD
  • ISBN 978-3-030-70603-6
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
About this book

This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors’ control of their critical data.

Table of contents (8 chapters)

Table of contents (8 chapters)
  • Federated Learning Research: Trends and Bibliometric Analysis

    Pages 1-19

    Farooq, Ali (et al.)

  • A Review of Privacy-Preserving Federated Learning for the Internet-of-Things

    Pages 21-50

    Briggs, Christopher (et al.)

  • Differentially Private Federated Learning: Algorithm, Analysis and Optimization

    Pages 51-78

    Wei, Kang (et al.)

  • Advancements of Federated Learning Towards Privacy Preservation: From Federated Learning to Split Learning

    Pages 79-109

    Thapa, Chandra (et al.)

  • PySyft: A Library for Easy Federated Learning

    Pages 111-139

    Ziller, Alexander (et al.)

Buy this book

eBook $119.00
price for USA in USD
  • ISBN 978-3-030-70604-3
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $159.99
price for USA in USD
  • ISBN 978-3-030-70603-6
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
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Bibliographic Information

Bibliographic Information
Book Title
Federated Learning Systems
Book Subtitle
Towards Next-Generation AI
Editors
  • Muhammad Habib ur Rehman
  • Mohamed Medhat Gaber
Series Title
Studies in Computational Intelligence
Series Volume
965
Copyright
2021
Publisher
Springer International Publishing
Copyright Holder
The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
eBook ISBN
978-3-030-70604-3
DOI
10.1007/978-3-030-70604-3
Hardcover ISBN
978-3-030-70603-6
Series ISSN
1860-949X
Edition Number
1
Number of Pages
XVI, 196
Number of Illustrations
3 b/w illustrations, 42 illustrations in colour
Topics