Private FL-GAN: Differential Privacy Synthetic Data Generation Based on Federated Learning
Published: May 1, 2020
Abstract
Generative Adversarial Network (GAN) has already made a big splash in the field of generating realistic "fake" data. However, when data is distributed and data-holders are reluctant to share data for privacy reasons, GAN’s training is difficult. To address this issue, we propose private FL-GAN, a differential privacy generative adversarial network model based on federated learning. By strategically combining the Lipschitz limit with the...
Paper Details
Title
Private FL-GAN: Differential Privacy Synthetic Data Generation Based on Federated Learning
Published Date
May 1, 2020
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