Federated and Transfer Learning

Roozbeh Razavi-Far

ISBN 10: 3031117506 / ISBN 13: 9783031117503

Published by Springer, Springer Okt 2023, 2023

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 380 pp. Englisch.

Seller Inventory # 9783031117503

Bibliographic Details

• Title: Federated and Transfer Learning
• Publisher: Springer, Springer Okt 2023
• Publication Date: 2023
• Language: English
• ISBN 10: 3031117506
• ISBN 13: 9783031117503
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 21D millimeters 235 millimeters width by 155 millimeters height by 21 millimeters depth
• Weight: 575 grams

About this title

Synopsis

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.