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Federated Learning

Privacy and Incentive
BuchKartoniert, Paperback
286 Seiten
Englisch
Springererschienen am26.11.20201st ed. 2020
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications.mehr
Verfügbare Formate
BuchKartoniert, Paperback
EUR80,24
E-BookPDF1 - PDF WatermarkE-Book
EUR80,24

Produkt

KlappentextThis book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications.
Zusammenfassung
Provides a comprehensive and self-contained introduction to Federated Learning

Popular topic for GDPR

Covers learning, implementation and practice of Federated Learning
Details
ISBN/GTIN978-3-030-63075-1
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2020
Erscheinungsdatum26.11.2020
Auflage1st ed. 2020
ReiheLecture Notes in Artificial Intelligence
Seiten286 Seiten
SpracheEnglisch
IllustrationenX, 286 p. 94 illus., 82 illus. in color.
Artikel-Nr.49048222

Inhalt/Kritik

Inhaltsverzeichnis
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.mehr

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