Research map / Federated learning and privacy / Privacy attacks and secure learning
Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train on synthetic images from distributed discriminators instead of a single real-data subset | AsynDGAN segmentation · BraTS2018 HGG 2D test | Dice: 0.6844 -> 0.7043 | Helped |
| Train on distributed synthetic images rather than all real images | AsynDGAN segmentation · BraTS2018 HGG 2D test | Dice: 0.7485 -> 0.7043 | Hurt |
| Use distributed discriminators rather than one discriminator for GAN synthesis | AsynDGAN segmentation · BraTS2018 HGG 2D test | Sens: 0.7099 -> 0.7295 | Helped |
| Use distributed synthetic images rather than all-real training data | AsynDGAN segmentation · BraTS2018 HGG 2D test | HD95: 12.85 -> 14.94 | Hurt |
| Train on synthetic images from distributed discriminators instead of a single real-data subset | AsynDGAN segmentation · Multi-Organ same-organ test | Dice: 0.7704 -> 0.7930 | Helped |
| Use distributed discriminators rather than one discriminator for GAN synthesis | AsynDGAN segmentation · Multi-Organ same-organ test | Dice: 0.7856 -> 0.7930 | Helped |
| Train on distributed synthetic images rather than all real images | AsynDGAN segmentation · Multi-Organ same-organ test | AJI: 0.5608 -> 0.5608 | No clear effect |
Closest papers by meaning
- PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees (ICLR 2019 · similarity 0.86)
- GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators (NeurIPS 2020 · similarity 0.86)
- ConfounderGAN: Protecting Image Data Privacy with Causal Confounder (NeurIPS 2022 · similarity 0.85)
- PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated Learning (AAAI 2024 · similarity 0.83)
- CG-TGAN: Conditional Generative Adversarial Networks with Graph Neural Networks for Tabular Data Synthesizing (AAAI 2025 · similarity 0.83)
- Real-Fake: Effective Training Data Synthesis Through Distribution Matching (ICLR 2024 · similarity 0.83)
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