Federated and Transfer Learning da Razavi-far, Roozbeh (Editor)/ Wang, Boyu (Editor)/ Taylor, Matthew E. (Editor)/ Yang, Qiang (Editor): Brand New Paperback (2023)

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379 pages. 9.26x6.10x0.78 inches. In Stock.

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Titolo: Federated and Transfer Learning
Editore: Springer Nature
Data di pubblicazione: 2023
Lingua: Inglese
ISBN 10: 3031117506
ISBN 13: 9783031117503
Rilegatura: Paperback
Condizione: Brand New

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Riassunto

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.

Dalla quarta di copertina

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.

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