no code implementations • 3 Jun 2024 • Jaeho Kim, Seok-Ju Hahn, Yoontae Hwang, Junghye Lee, Seulki Lee
This improvement in feature-wise ranking enhances our understanding of feature explainability in MTS.
no code implementations • 31 May 2024 • Seok-Ju Hahn, Gi-Soo Kim, Junghye Lee
Therefore, the need to achieve the client-level fairness in federated system has been emphasized, which can be realized by modifying the static aggregation scheme for updating the global model to an adaptive one, in response to the local signals of the participating clients.
1 code implementation • 16 Sep 2021 • 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 federated learning-based services.
Ranked #1 on Personalized Federated Learning on MNIST (ACC@1-100Clients metric)
no code implementations • 29 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.
no code implementations • 18 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.