Federated and Transfer Learning

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 a...

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Bibliographic Details
Other Authors: Razavi-Far, Roozbeh (Editor), Wang, Boyu (Editor), Taylor, Matthew E. (Editor), Yang, Qiang (Editor)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2023, 2023
Edition:1st ed. 2023
Series:Adaptation, Learning, and Optimization
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Description
Summary: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
Physical Description:VIII, 371 p. 90 illus., 80 illus. in color online resource
ISBN:9783031117480