Federated learning : privacy and incentive / Qiang Yang, Lixin Fan, Han Yu (eds.).
Yang, Qiang, 1961- editor.; Fan, Lixin, editor.; Yu, Han, editor.
2020
Q325.5
Available Online

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Title Federated learning : privacy and incentive / Qiang Yang, Lixin Fan, Han Yu (eds.).
ISBN 9783030630768 (electronic book)
3030630765 (electronic book)
9783030630751
DOI https://doi.org/10.1007/978-3-030-63076-8
Published Cham : Springer, [2020]
Language English
Description 1 online resource (291 pages).
Item Number 10.1007/978-3-030-63076-8 doi
Call Number Q325.5
Dewey Decimal Classification 006.3/1
Summary This book provides a comprehensive and self-contained introduction to Federated Learning, ranging from the basic knowledge and theories to various key applications, and the privacy and incentive factors are the focus of the whole book. This book is timely needed since Federated Learning is getting popular after the release of the General Data Protection Regulation (GDPR). As Federated Learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. First, it introduces different privacy-preserving methods for protecting a Federated Learning model against different types of attacks such as Data Leakage and/or Data Poisoning. Second, the book presents incentive mechanisms which aim to encourage individuals to participate in the Federated Learning ecosystems. Last but not the least, this book also describes how Federated Learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both academia and industries, who would like to learn federated learning from scratch, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing are preferred.
Note Includes author index.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Yang, Qiang, 1961- editor.
Fan, Lixin, editor.
Yu, Han, editor.
Series Lecture notes in computer science ; 12500.
Lecture notes in computer science. Lecture notes in artificial intelligence.
Available in Other Form Federated Learning : Privacy and Incentive
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
Privacy
Threats to Federated Learning
Rethinking Gradients Safety in Federated Learning
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks
Task-Agnostic Privacy-Preserving Representation Learning via Federated Learning
Large-Scale Kernel Method for Vertical Federated Learning
Towards Byzantine-resilient Federated Learning via Group-wise Robust Aggregation
Federated Soft Gradient Boosting Machine for Streaming Data
Dealing with Label Quality Disparity In Federated Learning
Incentive
FedCoin: A Peer-to-Peer Payment System for Federated Learning
Efficient and Fair Data Valuation for Horizontal Federated Learning
A Principled Approach to Data Valuation for Federated Learning
A Gamified Research Tool for Incentive Mechanism Design in Federated Learning
Budget-bounded Incentives for Federated Learning
Collaborative Fairness in Federated Learning
A Game-Theoretic Framework for Incentive Mechanism Design in Federated Learning
Applications
Federated Recommendation Systems
Federated Learning for Open Banking
Building ICU In-hospital Mortality Prediction Model with Federated Learning
Privacy-preserving Stacking with Application to Cross-organizational Diabetes Prediction.

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