Search Results for author: Junghye Lee

Found 3 papers, 0 papers with code

GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model

no code implementations29 Aug 2020 Seok-Ju Hahn, Junghye Lee

Unlike conventional federated learning algorithms based on gradients, our framework does not require to disassemble a model (i. e., to linear components) or to perturb data (or encryption of data for aggregation) to preserve privacy.

Dimensionality Reduction Federated Learning

Secure and Differentially Private Bayesian Learning on Distributed Data

no code implementations22 May 2020 Yeongjae Gil, Xiaoqian Jiang, Miran Kim, Junghye Lee

Data integration and sharing maximally enhance the potential for novel and meaningful discoveries.

Survival Analysis

Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Classification of Clinical Data

no code implementations18 Oct 2019 Seok-Ju Hahn, Junghye Lee

PhysioNet2012, a dataset for prediction of mortality of patients in an Intensive Care Unit (ICU), was used to verify the performance of the proposed method.

Dimensionality Reduction Federated Learning +3

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