Federated Learning

Name: Federated Learning
Price: 1099 SEK
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Privacy and Incentive

Häftad, Engelska, 2020

AvQiang Yang,Lixin Fan,Han Yu

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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

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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.