Overview
- 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)
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Front Matter
Keywords
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
Bibliographic Information
Book Title: Federated and Transfer Learning
Editors: Roozbeh Razavi-Far, Boyu Wang, Matthew E. Taylor, Qiang Yang
Series Title: Adaptation, Learning, and Optimization
DOI: https://doi.org/10.1007/978-3-031-11748-0
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 2023
Hardcover ISBN: 978-3-031-11747-3Published: 01 October 2022
Softcover ISBN: 978-3-031-11750-3Published: 02 October 2023
eBook ISBN: 978-3-031-11748-0Published: 30 September 2022
Series ISSN: 1867-4534
Series E-ISSN: 1867-4542
Edition Number: 1
Number of Pages: VIII, 371
Number of Illustrations: 10 b/w illustrations, 80 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Machine Learning