Federated and Transfer Learning - Couverture rigide

Couverture rigide

ISBN 10 : 3031117476 ISBN 13 : 9783031117473

Editeur : Springer International Publishing AG, 2022

Synopsis

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.

Détails de l'édition

• Éditeur: Springer International Publishing AG
• Date d'édition: 2022
• Langue: anglais
• ISBN 10: 3031117476
• ISBN 13: 9783031117473
• Reliure: Relié
• Numéro d'édition: 1
• Nombre de pages: 371
• Éditeur: Razavi-Far Roozbeh, Wang Boyu, Taylor Matthew E., Yang Qiang
• Coordonnées du fabricant: non disponible
• Personne responsable: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Allemagne