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Federated and Transfer Learning

  • Book
  • © 2023


  • Helps readers to understand transfer learning in conjunction with federated learning
  • Bridges the gap between transfer learning and federated learning
  • Performs a comprehensive study on the recent advancements and challenges in TL and FL

Part of the book series: Adaptation, Learning, and Optimization (ALO, volume 27)

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Table of contents (15 chapters)


About this book

This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.

Editors and Affiliations

  • Faculty of Computer Science, University of New Brunswick, Fredericton, Canada

    Roozbeh Razavi-Far

  • Department of Computer Science, Western University, London, Canada

    Boyu Wang

  • University of Alberta and the Alberta Machine Intelligence Institute (Amii), Edmonton, Canada

    Matthew E. Taylor

  • Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Kowloon, China

    Qiang Yang

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