Téléchargez le livre : Federated and Transfer Learning

Federated and Transfer Learning

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Éditeur :

Springer


Collection :

Adaptation, Learning, and Optimization

Paru le : 2022-09-30

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Description

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.
Pages
371 pages
Collection
Adaptation, Learning, and Optimization
Parution
2022-09-30
Marque
Springer
EAN papier
9783031117473
EAN PDF
9783031117480
Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
37
Taille du fichier
13363 Ko
Prix
147,69 €
EAN EPUB
9783031117480
Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
37
Taille du fichier
45148 Ko
Prix
147,69 €

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