Federated and Transfer Learning - Couverture rigide

9783031117473: Federated and Transfer Learning

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ISBN 10 : 3031117476 ISBN 13 : 9783031117473

Editeur : Springer International Publishing AG, 2022

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An Introduction to Federated and Transfer Learning.- Federated Learning for Resource-Constrained IoT Devices: Panoramas and State of the Art.- Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms.- Cross-silo Federated Neural Architecture Search for Heterogeneous and Cooperative Systems.- A Unifying Framework for Federated Learning.- A Contract Theory based Incentive Mechanism for Federated Learning.- A Study of Blockchain-based Federated Learning.- Swarm Meta Learning.- Rethinking Importance Weighting for Transfer Learning.- Transfer Learning via Representation Learning.- Modeling Individual Humans via a Secondary Task Transfer Learning Method.- From Theoretical to Practical Transfer Learning: The Adapt Library.- Lyapunov Robust Constrained-MDPs for Sim2Real Transfer Learning.- A Study on Efficient Reinforcement Learning Through Knowledge Transfer.- Federated Transfer Reinforcement Learning for Autonomous Driving.

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�diteurSpringer International Publishing AG
Date d'�dition2022
ISBN 10 3031117476
ISBN 13 9783031117473
ReliureReli�
Langueanglais
Num�ro d'�dition1
Nombre de pages371
�diteurRazavi-Far Roozbeh, Wang Boyu, Taylor Matthew E., Yang Qiang
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9783031117503: Federated and Transfer Learning

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ISBN 10 : 3031117506 ISBN 13 : 9783031117503
Editeur : Springer, 2023
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Federated and Transfer Learning

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 w. N� de r�f. du vendeur 628807018

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Federated and Transfer Learning

Razavi-Far, Roozbeh (Edited by)/ Wang, Boyu (Edited by)/ Taylor, Matthew E. (Edited by)/ Yang, Qiang (Edited by)

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Federated and Transfer Learning

Roozbeh Razavi-Far

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ISBN 10 : 3031117476 ISBN 13 : 9783031117473

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Buch. Etat : Neu. 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. N� de r�f. du vendeur 9783031117473

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Federated and Transfer Learning

Roozbeh Razavi-Far

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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - 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. 380 pp. Englisch. N� de r�f. du vendeur 9783031117473

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Federated and Transfer Learning (Adaptation, Learning, and Optimization, 27)

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Federated and Transfer Learning

Roozbeh Razavi-Far

Edit� par Springer International Publishing Okt 2022, 2022

ISBN 10 : 3031117476 ISBN 13 : 9783031117473

Neuf Couverture rigide

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Buch. Etat : Neu. 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 380 pp. Englisch. N� de r�f. du vendeur 9783031117473

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