Overview

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.”  

Full Product Details

Author:   Qiang Yang ,  Lixin Fan ,  Han Yu
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Volume:   12500
Weight:   0.456kg
ISBN:  

9783030630751

ISBN 10:   3030630757
Pages:   286
Publication Date:   26 November 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

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. 

Reviews

Author Information

Tab Content 6

Author Website:  

Customer Reviews

Recent Reviews

No review item found!

Add your own review!

Countries Available

All regions