Federated Learning

Privacy and Incentive
Artikelnummer: 978-3-030-63075-1
Einband: Kartonierter Einband (Kt)
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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. 

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. 

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VerlagSpringer
EinbandKartonierter Einband (Kt)
Erscheinungsjahr2020
Seitenangabe296 S.
AusgabekennzeichenEnglisch
MasseH23.5 cm x B15.5 cm x D1.7 cm 452 g
ReiheLecture Notes in Computer Science
AutorYang, Qiang (Hrsg.) / Fan, Lixin (Hrsg.) / Yu, Han (Hrsg.)

Alle Bände der Reihe "Lecture Notes in Computer Science"

Über den Autor Qiang (Hrsg.) Yang

Qiang Yang, PhD, is a Full Professor at the College of Electrical Engineering at Zhejiang University, China. He is a Senior Member of the IEEE and a Distinguished Member of the China Computer Federation. His primary research interests include smart energy systems, learning-based optimization and control. Gang Huang, PhD, is an Assistant Professor at the College of Electrical Engineering at Zhejiang University, China. He is a Senior Member of the IEEE and a Senior Member of the China Computer Federation. His primary research interests include artificial intelligence for power and energy systems.

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