Federated Learning: Privacy and IncentiveQiang Yang, Lixin Fan, Han Yu Springer Nature, 2020 M11 25 - 286 pages This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since 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. Firstly, 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. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not 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 the academia and the industry, who would like to learn about federated learning, 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 would be helpful.”
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Contents
3 | |
17 | |
How to Evaluate and Thwart Privacy Attacks | 32 |
TaskAgnostic PrivacyPreserving Representation Learning via Federated Learning | 51 |
LargeScale Kernel Method for Vertical Federated Learning | 66 |
Towards ByzantineResilient Federated Learning via GroupWise Robust Aggregation | 81 |
Federated Soft Gradient Boosting Machine for Streaming Data | 93 |
Dealing with Label Quality Disparity in Federated Learning | 108 |
A Gamified Research Tool for Incentive Mechanism Design in Federated Learning | 168 |
BudgetBounded Incentives for Federated Learning | 176 |
Collaborative Fairness in Federated Learning | 189 |
A GameTheoretic Framework for Incentive Mechanism Design in Federated Learning | 205 |
Applications | 223 |
Federated Recommendation Systems | 225 |
Federated Learning for Open Banking | 240 |
Building ICU Inhospital Mortality Prediction Model with Federated Learning | 255 |
Incentive | 122 |
A PeertoPeer Payment System for Federated Learning | 123 |
Efficient and Fair Data Valuation for Horizontal Federated Learning | 139 |
A Principled Approach to Data Valuation for Federated Learning | 153 |
PrivacyPreserving Stacking with Application to Crossorganizational Diabetes Prediction | 269 |
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Other editions - View all
Federated Learning Qiang Qiang Yang,Yang Yang Liu,Yong Yong Cheng,Yan Yan Kang,Tianjian Tianjian Chen,Han Han Yu Limited preview - 2022 |
Federated Learning Qiang Yang,Yang Liu,Yong Cheng,Yan Kang,Tianjian Chen,Han Yu Limited preview - 2019 |