49 citations · 88 across the 5 of their papers we have counts for
5 papers
FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
Gihun Lee, Minchan Jeong, Sangmook Kim +2
Federated Learning (FL) aggregates locally trained models from individual clients to construct a global model. While FL enables learning a model with data privacy, it often suffers…
Re-thinking Federated Active Learning based on Inter-class Diversity
SangMook Kim, Sangmin Bae, Hwanjun Song +1
Although federated learning has made awe-inspiring advances, most studies have assumed that the client's data are fully labeled. However, in a real-world scenario, every client may…
MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy
Gihun Lee, Sangmook Kim, Joonkee Kim +1
Cell segmentation is a fundamental task for computational biology analysis. Identifying the cell instances is often the first step in various downstream biomedical studies. However…
FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning
SangMook Kim, Wonyoung Shin, Soohyuk Jang +2
Robustness is becoming another important challenge of federated learning in that the data collection process in each client is naturally accompanied by noisy labels. However, it is…
FedBABU: Towards Enhanced Representation for Federated Image Classification
Jaehoon Oh, Sangmook Kim, Se-Young Yun
Federated learning has evolved to improve a single global model under data heterogeneity (as a curse) or to develop multiple personalized models using data heterogeneity (as a bles…