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Federated Learning

A Comprehensive Overview of Methods and Applications

  • First major book on Federated Learning, and the standard text on the topic by the leading researchers worldwide

  • Federated Learning as a concept is only a few years old but has seen a rapid increase in interest in the topic

  • Enables the reader to get a broad state-of-the-art summary of the most recent research developments

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eBook USD 119.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-96896-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book USD 159.99
Price excludes VAT (USA)

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Table of contents (25 chapters)

  1. Front Matter

    Pages i-ix
  2. Introduction to Federated Learning

    • Heiko Ludwig, Nathalie Baracaldo
    Pages 1-23
  3. Part I

    1. Front Matter

      Pages 25-26
    2. Tree-Based Models for Federated Learning Systems

      • Yuya Jeremy Ong, Nathalie Baracaldo, Yi Zhou
      Pages 27-52
    3. Semantic Vectorization: Text- and Graph-Based Models

      • Shalisha Witherspoon, Dean Steuer, Nirmit Desai
      Pages 53-70
    4. Personalization in Federated Learning

      • Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun
      Pages 71-98
    5. Personalized, Robust Federated Learning with Fed+

      • Pengqian Yu, Achintya Kundu, Laura Wynter, Shiau Hong Lim
      Pages 99-123
    6. Communication-Efficient Distributed Optimization Algorithms

      • Gauri Joshi, Shiqiang Wang
      Pages 125-143
    7. Communication-Efficient Model Fusion

      • Mikhail Yurochkin, Yuekai Sun
      Pages 145-176
    8. Federated Learning and Fairness

      • Annie Abay, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig
      Pages 177-191
  4. Part II

    1. Front Matter

      Pages 193-193
    2. Introduction to Federated Learning Systems

      • Syed Zawad, Feng Yan, Ali Anwar
      Pages 195-212
    3. Local Training and Scalability of Federated Learning Systems

      • Syed Zawad, Feng Yan, Ali Anwar
      Pages 213-233
    4. Straggler Management

      • Syed Zawad, Feng Yan, Ali Anwar
      Pages 235-258
    5. Systems Bias in Federated Learning

      • Syed Zawad, Feng Yan, Ali Anwar
      Pages 259-278
  5. Part III

    1. Front Matter

      Pages 279-279
    2. Private Parameter Aggregation for Federated Learning

      • K. R. Jayaram, Ashish Verma
      Pages 313-336
    3. Data Leakage in Federated Learning

      • Xiao Jin, Pin-Yu Chen, Tianyi Chen
      Pages 337-361
    4. Security and Robustness in Federated Learning

      • Ambrish Rawat, Giulio Zizzo, Muhammad Zaid Hameed, Luis Muñoz-González
      Pages 363-390

About this book

Federated Learning: A Comprehensive Overview of Methods and Applications presents an in-depth discussion of the most important issues and approaches to federated learning for researchers and practitioners. 

Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. Data are retained by data parties that participate in the FL process and are not shared with any other entity. This makes FL an increasingly popular solution for machine learning tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory or practical reasons.

This book explains recent progress in research and the state-of-the-art development of Federated Learning (FL), from the initial conception of the field to first applications and commercial use. To obtain this broad and deep overview, leading researchers address the different perspectives of federated learning: the core machine learning perspective, privacy and security, distributed systems, and specific application domains. Readers learn about the challenges faced in each of these areas, how they are interconnected, and how they are solved by state-of-the-art methods.

Following an overview on federated learning basics in the introduction, over the following 24 chapters, the reader will dive deeply into various topics. The first part addresses algorithmic questions of solving different machine learning tasks in a federated way and how to train efficiently, at scale, and fairly. Another part focuses on providing clarity on how to select privacy and security solutions in a way that can be tailored to specific use cases, while another considers the pragmatics of the systems where the federated learning process will run. The book also covers other important use cases for federated learning, such as split learning and vertical federated learning. Finally, the book includes some chapters focusing on applying FL in real-world enterprise settings.


Keywords

  • Deep Learning
  • Machine Learning
  • Artificial Intelligence
  • Vertically Partitioned Federated Learning
  • Neural network fusion
  • Federated reinforcement learning

Editors and Affiliations

  • IBM Research – Almaden, San Jose, USA

    Heiko Ludwig

  • IBM Research -- Almaden, San Jose, USA

    Nathalie Baracaldo

About the editors

Heiko Ludwig is a Senior Manager, AI Platforms and a Principal Research Staff Member at IBM’s Almaden Research Center in San Jose, CA. Heiko coordinates the Federated Learning program at IBM Research and oversees the Distributed AI research area. His research contributed to different products, including IBM’s machine learning products. He is an ACM Distinguished Engineer and has more than 150 publications with more than 8000 citations. His technical work led to a number of technical awards by IBM and his numerous patents and patent applications received a designation as an IBM Master Inventor. Heiko is a co-editor in chief of the International Journal of Cooperative Information Systems and serves on the editorial boards of multiple journals. Heiko also serves regularly as program committee chair in conferences in the field. Heiko's wider interest is on large scale and cross-organizational AI systems and its related distributed systems, security and privacy research issues. Heiko received a doctorate in information systems from Otto-Friedrich-Universität Bamberg, Germany.

Nathalie Baracaldo leads the AI Security and Privacy Solutions team and is a Research Staff Member at IBM’s Almaden Research Center in San Jose, CA. Nathalie is passionate about delivering machine learning solutions that are highly accurate, withstand adversarial attacks and protect data privacy. Nathalie has led her team to the design of IBM Federated Learning framework which is now part of the Watson Machine Learning product and continues to work on its expansion. In 2020, Nathalie received the IBM Master Inventor distinction for her contributions to the IBM Intellectual Property and innovation.  Nathalie also received the 2021 Corporate Technical Recognition, one of the highest recognitions provided to IBMers for breakthrough technical achievements that have led to notable market and industry success for IBM. This recognition was awarded for Nathalie's contribution to the Trusted AI Initiative. Nathalie has been invited to give multiple talks on federated learning, its challenges and opportunities. Nathalie has received four best paper awards and published in top-tier conferences and journals, obtaining more than 1300 Google scholar citations. Nathalie’s wider research interests include security and privacy, distributed systems and machine learning. Nathalie is also Associate Editor of the IEEE Transactions on Service Computing. Nathalie received her Ph.D. degree from the University of Pittsburgh in 2016.

Bibliographic Information

  • Book Title: Federated Learning

  • Book Subtitle: A Comprehensive Overview of Methods and Applications

  • Editors: Heiko Ludwig, Nathalie Baracaldo

  • DOI: https://doi.org/10.1007/978-3-030-96896-0

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-030-96895-3

  • eBook ISBN: 978-3-030-96896-0

  • Edition Number: 1

  • Number of Pages: VI, 534

  • Number of Illustrations: 17 b/w illustrations, 117 illustrations in colour

  • Topics: Artificial Intelligence, Machine Learning

Buying options

eBook USD 119.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-96896-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book USD 159.99
Price excludes VAT (USA)