21 citations · 30 across the 4 of their papers we have counts for
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cs.LG2021★ 21 cited
Connecting Low-Loss Subspace for Personalized Federated Learning
Seok-Ju Hahn, Minwoo Jeong, Junghye Lee
Due to the curse of statistical heterogeneity across clients, adopting a personalized federated learning method has become an essential choice for the successful deployment of fede…
cs.LG2020★ 1 cited
GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model
Seok-Ju Hahn, Junghye Lee
Federated learning platforms are gaining popularity. One of the major benefits is to mitigate the privacy risks as the learning of algorithms can be achieved without collecting or…
cs.LG2019★ 7 cited
Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Classification of Clinical Data
Seok-Ju Hahn, Junghye Lee
In clinical research, the lack of events of interest often necessitates imbalanced learning. One approach to resolve this obstacle is data integration or sharing, but due to privac…