Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach

Understand

Recently, Generative Adversarial Networks (GANs) have demonstrated their potential in federated learning, i.e., learning a centralized model from data privately hosted by multiple sites.

• A federatedGAN jointly trains a centralized generator and multiple private discriminators hosted at different sites.
• A major theoretical challenge for the federated GAN is the heterogeneity of the local data distributions.
• Traditional approaches cannot guarantee to learn the target distribution, which isa mixture of the highly different local distributions.