mlsanityData to 7 Oct 2026

Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data

arXiv 2006.00080 ↗First posted 29 May 2020eess.IV cs.CV cs.LGcited by 38

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Abstract (arXiv)

In this paper, we propose a data privacy-preserving and communication efficient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN). Our proposed framework aims to train a central generator learns from distributed discriminator, and use the generated synthetic image solely to train the segmentation model.We validate the proposed framework on the application of health entities learning problem which is known to be privacy sensitive. Our experiments show that our approach: 1) could learn the real image's distribution from multiple datasets without sharing the patient's raw data. 2) is more efficient and requires lower bandwidth than other distributed deep learning methods. 3) achieves higher performance compared to the model trained by one real dataset, and almost the same performance compared to the model trained by all real datasets. 4) has provable guarantees that the generator could learn the distributed distribution in an all important fashion thus is unbiased.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 May 2020.

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Citing papers per year: 2021: 9; 2022: 10; 2023: 6; 2024: 8; 2025: 5.

38 papers.

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  1. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  2. 2021Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformerscited by 76
  3. 2021Federated Semi-supervised Medical Image Classification via Inter-client Relation Matchingcited by 42
  4. 2022Federated Learning of Generative Image Priors for MRI Reconstructioncited by 33
  5. 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
  6. 2021FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumoniacited by 22
  7. 2021Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentationcited by 19
  8. 2021Data synthesis and adversarial networks: A review and meta-analysis in cancer imagingcited by 15
  9. 2022Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesiscited by 12
  10. 2022SS-3DCapsNet: Self-supervised 3D Capsule Networks for Medical Segmentation on Less Labeled Datacited by 10
  11. 2021Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Datacited by 9
  12. 2023Topology-Guided Multi-Class Cell Context Generation for Digital Pathologycited by 7
26 more
  1. 2023VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarkscited by 6
  2. 2023Federated Learning via Input-Output Collaborative Distillationcited by 6
  3. 2024Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspectivecited by 6
  4. 2024FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesiscited by 6
  5. 2022Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classificationcited by 5
  6. 2021AdaptCL: Efficient Collaborative Learning with Dynamic and Adaptive Pruningcited by 4
  7. 2023Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beatcited by 4
  8. 2024FLIGAN: Enhancing Federated Learning with Incomplete Data using GANcited by 4
  9. 2025Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstructioncited by 4
  10. 2024Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistencycited by 3
  11. 2024Embarrassingly Parallel GFlowNetscited by 3
  12. 2025Frequency-Calibrated Membership Inference Attacks on Medical Image Diffusion Modelscited by 3
  13. 2022Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesionscited by 2
  14. 2022Modeling Global Distribution for Federated Learning with Label Distribution Skewcited by 2
  15. 2022Modality Bank: Learn multi-modality images across data centers without sharing medical datacited by 1
  16. 2022Attacks and Defenses for Free-Riders in Multi-Discriminator GANcited by 1
  17. 2025FedRIR: Rethinking Information Representation in Federated Learningcited by 1
  18. 2025A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neckcited by 1
  19. 2022MIPR:Automatic Annotation of Medical Images with Pixel Rearrangementcited by 0
  20. 2022Generative Adversarial Network Based Synthetic Learning and a Novel Domain Relevant Loss Term for Spine Radiographscited by 0
  21. 2023Distributed Conditional GAN (discGAN) For Synthetic Healthcare Data Generationcited by 0
  22. 2023SL: Stable Learning in Source-Free Domain Adaption for Medical Image Segmentationcited by 0
  23. 2024High-resolution segmentations of the hypothalamus and its subregions for training of segmentation modelscited by 0
  24. 2024Federated Black-Box Adaptation for Semantic Segmentationcited by 0
  25. 2024ParaGAN: A Scalable Distributed Training Framework for Generative Adversarial Networkscited by 0
  26. 2025Advances in Medical Image Segmentation: A Comprehensive Survey with a Focus on Lumbar Spine Applicationscited by 0