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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 7: A Study of Blockchain-Based Federated Learning
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Citations

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7 Mendeley
Summary Dimensions citations
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Chapter title
A Study of Blockchain-Based Federated Learning
Chapter number 7
Book title
Federated and Transfer Learning
Published by
Springer, Cham, January 2023
DOI 10.1007/978-3-031-11748-0_7
Book ISBNs
978-3-03-111747-3, 978-3-03-111748-0
Authors

Miri Rostami, Samaneh, Samet, Saeed, Kobti, Ziad

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Other 1 14%
Student > Master 1 14%
Unknown 5 71%
Readers by discipline Count As %
Computer Science 1 14%
Medicine and Dentistry 1 14%
Unknown 5 71%

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