9 citations · 28 across the 6 of their papers we have counts for
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cs.LG2021★ 7 cited
Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach
Yikai Zhang, Hui Qu, Qi Chang +3
Recently, Generative Adversarial Networks (GANs) have demonstrated their potential in federated learning, i.e., learning a centralized model from data privately hosted by multiple…
cs.LG2020★ 8 cited
Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information
Qi Chang, Zhennan Yan, Lohendran Baskaran +5
As deep learning technologies advance, increasingly more data is necessary to generate general and robust models for various tasks. In the medical domain, however, large-scale and…