Decentralized Learning of Generative Adversarial Networks from Multi-Client Non-iid Data

@article{Yonetani2019DecentralizedLO,
  title={Decentralized Learning of Generative Adversarial Networks from Multi-Client Non-iid Data},
  author={Ryo Yonetani and Tomohiro Takahashi and Atsushi Hashimoto and Yoshitaka Ushiku},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.09684}
}
This work addresses a new problem of learning generative adversarial networks (GANs) from multiple data collections that are each i) owned separately and privately by different clients and ii) drawn from a non-identical distribution that comprises different classes. Given such multi-client and non-iid data as input, we aim to achieve a distribution involving all the classes input data can belong to, while keeping the data decentralized and private in each client storage. Our key contribution to… CONTINUE READING

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