Federated and transfer learning / Roozbeh Razavi-Far, Boyu Wang, Matthew E. Taylor, Qiang Yang, editors.

Unlimited

Authorized users

Can lend chapters, not whole ebooks

Federated and transfer learning / Roozbeh Razavi-Far, Boyu Wang, Matthew E. Taylor, Qiang Yang, editors.

9783031117480 (electronic bk.)
3031117484 (electronic bk.)
3031117476
9783031117473

Cham, Switzerland : Springer, [2023]

English

1 online resource (1 volume) : illustrations (black and white, and color).

10.1007/978-3-031-11748-0 doi

Q325.787

006.3/1

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.

Access limited to authorized users.

Description based on print version record.

Adaptation, learning and optimization ; v. 27.

Online Resources > Ebooks
All Resources