We propose Federated Generative Adversarial Network (FedGAN) for training a GAN across distributed sources of non-independent-and-identically-distributed data sources subject to communication and privacy constraints. Our algorithm uses local generators and discriminators which are periodically synced via an intermediary that averages and broadcasts the generator and discriminator parameters. We theoretically prove the convergence of FedGAN with both equal and two time-scale updates of generator and discriminator, under standard assumptions, using stochastic approximations and communication efficient stochastic gradient descents. We experiment FedGAN on toy examples (2D system, mixed Gaussian, and Swiss role), image datasets (MNIST, CIFAR-10, and CelebA), and time series datasets (household electricity consumption and electric vehicle charging sessions). We show FedGAN converges and has similar performance to general distributed GAN, while reduces communication complexity. We also show its robustness to reduced communications.

本文提出了一种名为Federated Generative Adversarial Network (FedGAN)的算法，用于训练分布式非独立同分布数据源的 GAN，并基于周期性同步本地生成器和辨别器以及平均和广播生成器和辨别器参数的中介。我们证明了 FedGAN 在标准假设下，使用随机近似和通信高效的随机梯度下降法，使用相等和两个时间尺度更新生成器和辨别器收敛。我们在玩具示例（2D 系统、混合高斯和瑞士拳击手数据集）、图像数据集（MNIST、CIFAR-10 和 CelebA）以及时间序列数据集（家庭电力消耗和电动汽车充电会话）上实验了 FedGAN。我们展示了 FedGAN 收敛并且具有类似于一般分布式 GAN 的性能，同时可以减少通信复杂性。此外，我们还展示了它对通信减少的鲁棒性。

FedGAN：用于分布式数据的联邦生成对抗网络