Decentralized Learning of Generative Adversarial Networks from Non-iid Data

no code implementations • 23 May 2019 • Ryo Yonetani, Tomohiro Takahashi, Atsushi Hashimoto, Yoshitaka Ushiku

This work addresses a new problem that learns generative adversarial networks (GANs) from multiple data collections that are each i) owned separately by different clients and ii) drawn from a non-identical distribution that comprises different classes.

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