Fed-DPSDG-WGAN: Differentially Private Synthetic Data Generation for Loan Default Prediction via Federated Wasserstein GAN

Lenders typically conduct thorough credit checks to mitigate credit default risk before lending money. Proactively predicting loan defaulters has become increasingly important. However, creating robust deep-learning algorithms that classify loan defaulters requires abundant data, potentially comprom...

書誌詳細
出版年:	IEEE Access
主要な著者:	Padmaja Ramachandra, Santhi Vaithiyanathan
フォーマット:	論文
言語:	英語
出版事項:	IEEE 2025-01-01
主題:	Federated learning WGAN CTGAN differentially private data oversampling trivial auto-encoder
オンライン･アクセス:	https://ieeexplore.ieee.org/document/10930895/

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