# MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets

arXiv: https://arxiv.org/abs/1811.03850 · HTML: https://mlsanity.com/paper/1811.03850/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2018.

## Built on by (most cited first, 30 of 49)

- [Diffusion-Based Representation Learning](https://mlsanity.com/paper/2105.14257/) (2021)
- [FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning](https://mlsanity.com/paper/2111.08211/) (2021)
- [Generative adversarial networks in time series: A survey and taxonomy](https://mlsanity.com/paper/2107.11098/) (2021)
- [Generative Adversarial Networks: A Survey Towards Private and Secure Applications](https://mlsanity.com/paper/2106.03785/) (2021)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary](https://mlsanity.com/paper/2101.07235/) (2021)
- [FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networks](https://mlsanity.com/paper/2201.03169/) (2022)
- [Training Mixed-Domain Translation Models via Federated Learning](https://mlsanity.com/paper/2205.01557/) (2022)
- [Consistent Distributed Ranking of Generative Models via Kernel Distances](https://mlsanity.com/paper/2310.11714/) (2023)
- [Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data](https://mlsanity.com/paper/2108.07927/) (2021)
- [Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks](https://mlsanity.com/paper/2102.01751/) (2021)
- [Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach](https://mlsanity.com/paper/2102.04655/) (2021)
- [FedSyn: Synthetic Data Generation using Federated Learning](https://mlsanity.com/paper/2203.05931/) (2022)
- [Federated Learning for Diffusion Models](https://mlsanity.com/paper/2503.06426/) (2025)
- [Game of GANs: Game-Theoretical Models for Generative Adversarial Networks](https://mlsanity.com/paper/2106.06976/) (2021)
- [FLAIM: AIM-based Synthetic Data Generation in the Federated Setting](https://mlsanity.com/paper/2310.03447/) (2023)
- [Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation](https://mlsanity.com/paper/2205.12445/) (2022)
- [Federated Split GANs](https://mlsanity.com/paper/2207.01750/) (2022)
- [CollaFuse: Collaborative Diffusion Models](https://mlsanity.com/paper/2406.14429/) (2024)
- [Bi-Discriminator Class-Conditional Tabular GAN](https://mlsanity.com/paper/2111.06549/) (2021)
- [EFFGAN: Ensembles of fine-tuned federated GANs](https://mlsanity.com/paper/2206.11682/) (2022)
- [PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models](https://mlsanity.com/paper/2503.08085/) (2025)
- [Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence](https://mlsanity.com/paper/2101.09225/) (2021)
- [MULTI-FLGANs: Multi-Distributed Adversarial Networks for Non-IID distribution](https://mlsanity.com/paper/2206.12178/) (2022)
- [Modeling Global Distribution for Federated Learning with Label Distribution Skew](https://mlsanity.com/paper/2212.08883/) (2022)
- [MAAIP: Multi-Agent Adversarial Interaction Priors for imitation from fighting demonstrations for physics-based characters](https://mlsanity.com/paper/2311.02502/) (2023)
- [Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets](https://mlsanity.com/paper/2103.09876/) (2021)
- [Federated Traffic Synthesizing and Classification Using Generative Adversarial Networks](https://mlsanity.com/paper/2104.10400/) (2021)
- [Towards convergence to Nash equilibria in two-team zero-sum games](https://mlsanity.com/paper/2111.04178/) (2021)
- [Attacks and Defenses for Free-Riders in Multi-Discriminator GAN](https://mlsanity.com/paper/2201.09967/) (2022)

## Abstract (arXiv)

A recent technical breakthrough in the domain of machine learning is the discovery and the multiple applications of Generative Adversarial Networks (GANs). Those generative models are computationally demanding, as a GAN is composed of two deep neural networks, and because it trains on large datasets. A GAN is generally trained on a single server. In this paper, we address the problem of distributing GANs so that they are able to train over datasets that are spread on multiple workers. MD-GAN is exposed as the first solution for this problem: we propose a novel learning procedure for GANs so that they fit this distributed setup. We then compare the performance of MD-GAN to an adapted version of Federated Learning to GANs, using the MNIST and CIFAR10 datasets. MD-GAN exhibits a reduction by a factor of two of the learning complexity on each worker node, while providing better performances than federated learning on both datasets. We finally discuss the practical implications of distributing GANs.