1 citations · 1 across the 3 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2024
GeFL: Model-Agnostic Federated Learning with Generative Models
Honggu Kang, Seohyeon Cha, Joonhyuk Kang
Federated learning (FL) is a distributed training paradigm that enables collaborative learning across clients without sharing local data, thereby preserving privacy. However, the i…
cs.LG2023★ 1 cited
On the Temperature of Bayesian Graph Neural Networks for Conformal Prediction
Seohyeon Cha, Honggu Kang, Joonhyuk Kang
Accurate uncertainty quantification in graph neural networks (GNNs) is essential, especially in high-stakes domains where GNNs are frequently employed. Conformal prediction (CP) of…
cs.LG2023
NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients
Honggu Kang, Seohyeon Cha, Jinwoo Shin +2
Federated learning (FL) enables distributed training while preserving data privacy, but stragglers-slow or incapable clients-can significantly slow down the total training time and…