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.
Roozbeh Razavi-Far & Boyu Wang
Federated and Transfer Learning [PDF ebook]
Federated and Transfer Learning [PDF ebook]
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Langue Anglais ● Format PDF ● Pages 371 ● ISBN 9783031117480 ● Taille du fichier 13.7 MB ● Éditeur Roozbeh Razavi-Far & Boyu Wang ● Maison d’édition Springer International Publishing ● Lieu Cham ● Pays CH ● Publié 2022 ● Téléchargeable 24 mois ● Devise EUR ● ID 8647924 ● Protection contre la copie DRM sociale