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Federated and Transfer Learning

Overview of attention for book
Cover of 'Federated and Transfer Learning'

Table of Contents

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    Book Overview
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    Chapter 1 An Introduction to Federated and Transfer Learning
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    Chapter 2 Federated Learning for Resource-Constrained IoT Devices: Panoramas and State of the Art
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    Chapter 3 Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms
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    Chapter 4 Cross-Silo Federated Neural Architecture Search for Heterogeneous and Cooperative Systems
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    Chapter 5 A Unifying Framework for Federated Learning
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    Chapter 6 A Contract Theory Based Incentive Mechanism for Federated Learning
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    Chapter 7 A Study of Blockchain-Based Federated Learning
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    Chapter 8 Swarm Meta Learning
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    Chapter 9 Rethinking Importance Weighting for Transfer Learning
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    Chapter 10 Transfer Learning via Representation Learning
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    Chapter 11 Modeling Individual Humans via a Secondary Task Transfer Learning Method
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    Chapter 12 From Theoretical to Practical Transfer Learning: The ADAPT Library
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    Chapter 13 Lyapunov Robust Constrained-MDPs for Sim2Real Transfer Learning
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    Chapter 14 A Study on Efficient Reinforcement Learning Through Knowledge Transfer
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    Chapter 15 Federated Transfer Reinforcement Learning for Autonomous Driving
Attention for Chapter 9: Rethinking Importance Weighting for Transfer Learning
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Citations

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Summary Dimensions citations
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Chapter title
Rethinking Importance Weighting for Transfer Learning
Chapter number 9
Book title
Federated and Transfer Learning
Published by
Springer, Cham, January 2023
DOI 10.1007/978-3-031-11748-0_9
Book ISBNs
978-3-03-111747-3, 978-3-03-111748-0
Authors

Lu, Nan, Zhang, Tianyi, Fang, Tongtong, Teshima, Takeshi, Sugiyama, Masashi

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 27%
Student > Bachelor 3 14%
Researcher 2 9%
Student > Master 2 9%
Professor 1 5%
Other 1 5%
Unknown 7 32%
Readers by discipline Count As %
Computer Science 8 36%
Mathematics 4 18%
Engineering 2 9%
Social Sciences 1 5%
Unknown 7 32%

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