Federated learning : privacy and incentive / Qiang Yang, Lixin Fan, Han Yu (eds.)
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 p...
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| Online Access: |
Full Text (via Springer) |
|---|---|
| Other Authors: | , , |
| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer,
[2020]
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| Series: | Lecture notes in computer science ;
12500. Lecture notes in computer science. Lecture notes in artificial intelligence. |
| Subjects: |
Table of Contents:
- 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.