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We present a novel method that supports the learning of multiple classification tasks from geographically distributed data. By combining locally trained generative adversarial networks (GANs) with a small fraction of original data samples, our proposed scheme can train multiple discriminative models at a central location with low communication overhead. Experiments using common image datasets (MNIST, CIFAR-10, LSUN-20, Celeb-A) show that our proposed scheme can achieve comparable classification accuracy as the ideal classifier trained using all data from all sites.