Name:
FeGAN
Description:
Scaling distributed Generative Adversarial Networks (GANs)
Professor — Lab:
Rachid GuerraouiDistributed Computing Lab

Technical description:
The FeGAN system enables training GANs in the Federated Learning setup. GANs are generative adversarial networks, a class of machine learning where two neural networks contest with each other. FeGAN is implemented on PyTorch and is using less bandwidth and is faster than using stat-of-the-art GANs implementations.
Papers:
Project status:
inactive — entered showcase: 2020-11-11 — entry updated: 2024-03-22

Source code:
Lab GitHub - last commit: 2020-12-31
Code quality:
Prototype
Project type:
Application
Programming language:
Python
License:
MIT