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Federated Learning

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Cover of 'Federated Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Federated Learning
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    Chapter 2 Tree-Based Models for Federated Learning Systems
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    Chapter 3 Semantic Vectorization: Text- and Graph-Based Models
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    Chapter 4 Personalization in Federated Learning
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    Chapter 5 Personalized, Robust Federated Learning with Fed+
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    Chapter 6 Communication-Efficient Distributed Optimization Algorithms
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    Chapter 7 Communication-Efficient Model Fusion
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    Chapter 8 Federated Learning and Fairness
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    Chapter 9 Introduction to Federated Learning Systems
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    Chapter 10 Local Training and Scalability of Federated Learning Systems
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    Chapter 11 Straggler Management
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    Chapter 12 Systems Bias in Federated Learning
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    Chapter 13 Protecting Against Data Leakage in Federated Learning: What Approach Should You Choose?
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    Chapter 14 Private Parameter Aggregation for Federated Learning
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    Chapter 15 Data Leakage in Federated Learning
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    Chapter 16 Security and Robustness in Federated Learning
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    Chapter 17 Dealing with Byzantine Threats to Neural Networks
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    Chapter 18 Privacy-Preserving Vertical Federated Learning
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    Chapter 19 Split Learning: A Resource Efficient Model and Data Parallel Approach for Distributed Deep Learning
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    Chapter 20 Federated Learning for Collaborative Financial Crimes Detection
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    Chapter 21 Federated Reinforcement Learning for Portfolio Management
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    Chapter 22 Application of Federated Learning in Medical Imaging
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    Chapter 23 Advancing Healthcare Solutions with Federated Learning
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    Chapter 24 A Privacy-preserving Product Recommender System
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    Chapter 25 Application of Federated Learning in Telecommunications and Edge Computing
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Title
Federated Learning
Published by
Springer Nature, July 2022
DOI 10.1007/978-3-030-96896-0
ISBNs
978-3-03-096896-0, 978-3-03-096895-3
Editors

Heiko Ludwig, Nathalie Baracaldo

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 16%
Researcher 2 8%
Student > Doctoral Student 2 8%
Lecturer 1 4%
Other 1 4%
Other 0 0%
Unknown 15 60%
Readers by discipline Count As %
Computer Science 7 28%
Chemical Engineering 1 4%
Medicine and Dentistry 1 4%
Engineering 1 4%
Unknown 15 60%