Federated and Transfer Learning
Qiang Yang editor Matthew E Taylor editor Roozbeh Razavi-Far editor Boyu Wang editor
Format:Hardback
Publisher:Springer International Publishing AG
Published:1st Oct '22
Currently unavailable, and unfortunately no date known when it will be back
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.
ISBN: 9783031117473
Dimensions: unknown
Weight: unknown
371 pages
1st ed. 2023