Federated and Transfer Learning

Roozbeh Razavi-Far

ISBN 10: 3031117476 / ISBN 13: 9783031117473

Published by Springer International Publishing, 2022

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783031117473

Bibliographic Details

• Title: Federated and Transfer Learning
• Publisher: Springer International Publishing
• Publication Date: 2022
• Language: English
• ISBN 10: 3031117476
• ISBN 13: 9783031117473
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 26D millimeters 241 millimeters width by 160 millimeters height by 26 millimeters depth
• Weight: 735 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.