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Federated Learning Systems

Towards Next-Generation AI

  • Book
  • © 2021

Overview

Editors:
  1. Muhammad Habib ur Rehman

    1. Center for Cyber-Physical Systems, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates

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  2. Mohamed Medhat Gaber

    1. School of Computing and Digital Technology, Birmingham City University, Birmingham, UK

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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

Part of the book series: Studies in Computational Intelligence (SCI, volume 965)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xvi
    Download chapter PDF

  2. Federated Learning Research: Trends and Bibliometric Analysis

    • Ali Farooq, Ali Feizollah, Muhammad Habib ur Rehman
    Pages 1-19

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

    • Christopher Briggs, Zhong Fan, Peter Andras
    Pages 21-50

  4. Differentially Private Federated Learning: Algorithm, Analysis and Optimization

    • Kang Wei, Jun Li, Chuan Ma, Ming Ding, H. Vincent Poor
    Pages 51-78

  5. Advancements of Federated Learning Towards Privacy Preservation: From Federated Learning to Split Learning

    • Chandra Thapa, M. A. P. Chamikara, Seyit A. Camtepe
    Pages 79-109

  6. PySyft: A Library for Easy Federated Learning

    • Alexander Ziller, Andrew Trask, Antonio Lopardo, Benjamin Szymkow, Bobby Wagner, Emma Bluemke et al.
    Pages 111-139

  7. Federated Learning Systems for Healthcare: Perspective and Recent Progress

    • Yogesh Kumar, Ruchi Singla
    Pages 141-156

  8. Towards Blockchain-Based Fair and Trustworthy Federated Learning Systems

    • Ahmed Mukhtar Dirir, Khaled Salah, Davor Svetinovic
    Pages 157-171

  9. An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies

    • David Enthoven, Zaid Al-Ars
    Pages 173-196
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Keywords

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.

Editors and Affiliations

  • Center for Cyber-Physical Systems, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates

    Muhammad Habib ur Rehman

  • School of Computing and Digital Technology, Birmingham City University, Birmingham, UK

    Mohamed Medhat Gaber

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

  • DOI: https://doi.org/10.1007/978-3-030-70604-3

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

  • Hardcover ISBN: 978-3-030-70603-6Published: 12 June 2021

  • Softcover ISBN: 978-3-030-70606-7Published: 12 June 2022

  • eBook ISBN: 978-3-030-70604-3Published: 11 June 2021

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XVI, 196

  • Number of Illustrations: 3 b/w illustrations, 42 illustrations in colour

  • Topics: Computational Intelligence, Machine Learning

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