Federated and Transfer Learning (Hardcover, 1st ed. 2023)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Adaptation, Learning, and Optimization, 27
Release date:	October 2022
First published:	2023
Editors:	Roozbeh Razavi-Far • Boyu Wang • Matthew E. Taylor • Qiang Yang
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	371
Edition:	1st ed. 2023
ISBN-13:	978-3-03-111747-3
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	3-03-111747-6
Barcode:	9783031117473

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