Federated Learning

Privacy and Incentive

Häftad, Engelska, 2020

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This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications.

Produktinformation

Utgivningsdatum2020-11-26
Mått155 x 235 x 17 mm
Vikt452 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor286
FörlagSpringer Nature Switzerland AG
ISBN9783030630751

Tillhör följande kategorier

Tillämpad datateknik inom Data och it
Hårdvara inom Data och it
Artificiell intelligens inom Data och it
Nätverk och kommunikation inom Data och it
IT-säkerhet inom Data och it

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.