# Learn distributed GAN with Temporary Discriminators

arXiv: https://arxiv.org/abs/2007.09221 · HTML: https://mlsanity.com/paper/2007.09221/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data](https://mlsanity.com/paper/2108.07927/) (2021)
- [Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach](https://mlsanity.com/paper/2102.04655/) (2021)
- [Embarrassingly Parallel GFlowNets](https://mlsanity.com/paper/2406.03288/) (2024)
- [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)

## Abstract (arXiv)

In this work, we propose a method for training distributed GAN with sequential temporary discriminators. Our proposed method tackles the challenge of training GAN in the federated learning manner: How to update the generator with a flow of temporary discriminators? We apply our proposed method to learn a self-adaptive generator with a series of local discriminators from multiple data centers. We show our design of loss function indeed learns the correct distribution with provable guarantees. The empirical experiments show that our approach is capable of generating synthetic data which is practical for real-world applications such as training a segmentation model.