Federated Learning Privacy and Incentive /
Federated Learning Privacy and Incentive / [electronic resource] :
edited by Qiang Yang, Lixin Fan, Han Yu.
- 1st ed. 2020.
- X, 286 p. 94 illus., 82 illus. in color. online resource.
- Lecture Notes in Artificial Intelligence, 12500 2945-9141 ; .
- Lecture Notes in Artificial Intelligence, 12500 .
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. .
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 describeshow 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.
9783030630768
10.1007/978-3-030-63076-8 doi
Artificial intelligence.
Data protection.
Computer networks .
Social sciences--Data processing.
Application software.
Artificial Intelligence.
Data and Information Security.
Computer Communication Networks.
Computer Application in Social and Behavioral Sciences.
Computer and Information Systems Applications.
Q334-342 TA347.A78
006.3
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. .
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 describeshow 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.
9783030630768
10.1007/978-3-030-63076-8 doi
Artificial intelligence.
Data protection.
Computer networks .
Social sciences--Data processing.
Application software.
Artificial Intelligence.
Data and Information Security.
Computer Communication Networks.
Computer Application in Social and Behavioral Sciences.
Computer and Information Systems Applications.
Q334-342 TA347.A78
006.3