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

arXiv: https://arxiv.org/abs/2006.00080 · HTML: https://mlsanity.com/paper/2006.00080/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2020.

## Built on by (most cited first, 30 of 38)

- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
- [Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers](https://mlsanity.com/paper/2105.08059/) (2021)
- [Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching](https://mlsanity.com/paper/2106.08600/) (2021)
- [Federated Learning of Generative Image Priors for MRI Reconstruction](https://mlsanity.com/paper/2202.04175/) (2022)
- [FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks](https://mlsanity.com/paper/2111.11066/) (2021)
- [FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia](https://mlsanity.com/paper/2104.12581/) (2021)
- [Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation](https://mlsanity.com/paper/2104.10195/) (2021)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesis](https://mlsanity.com/paper/2210.10886/) (2022)
- [SS-3DCapsNet: Self-supervised 3D Capsule Networks for Medical Segmentation on Less Labeled Data](https://mlsanity.com/paper/2201.05905/) (2022)
- [Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data](https://mlsanity.com/paper/2108.07927/) (2021)
- [Topology-Guided Multi-Class Cell Context Generation for Digital Pathology](https://mlsanity.com/paper/2304.02255/) (2023)
- [VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks](https://mlsanity.com/paper/2307.02040/) (2023)
- [Federated Learning via Input-Output Collaborative Distillation](https://mlsanity.com/paper/2312.14478/) (2023)
- [Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspective](https://mlsanity.com/paper/2402.18607/) (2024)
- [FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis](https://mlsanity.com/paper/2412.06690/) (2024)
- [Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification](https://mlsanity.com/paper/2209.02625/) (2022)
- [Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction](https://mlsanity.com/paper/2502.04521/) (2025)
- [AdaptCL: Efficient Collaborative Learning with Dynamic and Adaptive Pruning](https://mlsanity.com/paper/2106.14126/) (2021)
- [Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat](https://mlsanity.com/paper/2309.03237/) (2023)
- [FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN](https://mlsanity.com/paper/2403.16930/) (2024)
- [Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency](https://mlsanity.com/paper/2403.12695/) (2024)
- [Embarrassingly Parallel GFlowNets](https://mlsanity.com/paper/2406.03288/) (2024)
- [Frequency-Calibrated Membership Inference Attacks on Medical Image Diffusion Models](https://mlsanity.com/paper/2506.14919/) (2025)
- [Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions](https://mlsanity.com/paper/2203.10507/) (2022)
- [Modeling Global Distribution for Federated Learning with Label Distribution Skew](https://mlsanity.com/paper/2212.08883/) (2022)
- [Modality Bank: Learn multi-modality images across data centers without sharing medical data](https://mlsanity.com/paper/2201.08955/) (2022)
- [Attacks and Defenses for Free-Riders in Multi-Discriminator GAN](https://mlsanity.com/paper/2201.09967/) (2022)
- [FedRIR: Rethinking Information Representation in Federated Learning](https://mlsanity.com/paper/2502.00859/) (2025)
- [A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck](https://mlsanity.com/paper/2506.08654/) (2025)

## 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.