2 citations · 2 across the 7 of their papers we have counts for
12 papers · 1 filter
Forecasting-based Biomedical Time-series Data Synthesis for Open Data and Robust AI
Youngjoon Lee, Seongmin Cho, Yehhyun Jo +3
The limited data availability due to strict privacy regulations and significant resource demands severely constrains biomedical time-series AI development, which creates a critical…
When to Stop Federated Learning: Zero-Shot Generation of Synthetic Validation Data with Generative AI for Early Stopping
Youngjoon Lee, Hyukjoon Lee, Jinu Gong +2
Federated Learning (FL) enables collaborative model training across decentralized devices while preserving data privacy. However, FL methods typically run for a predefined number o…
CG-FKAN: Compressed-Grid Federated Kolmogorov-Arnold Networks for Communication Constrained Environment
Seunghun Yu, Youngjoon Lee, Jinu Gong +1
Federated learning (FL), widely used in privacy-critical applications, suffers from limited interpretability, whereas Kolmogorov-Arnold Networks (KAN) address this limitation via l…
Debunking Optimization Myths in Federated Learning for Medical Image Classification
Youngjoon Lee, Hyukjoon Lee, Jinu Gong +2
Federated Learning (FL) is a collaborative learning method that enables decentralized model training while preserving data privacy. Despite its promise in medical imaging, recent F…
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output Codes
Youngjoon Lee, Jinu Gong, Joonhyuk Kang
Kolmogorov-Arnold Networks (KAN) offer universal function approximation using univariate spline compositions without nonlinear activations. In this work, we integrate Error-Correct…
A Unified Benchmark of Federated Learning with Kolmogorov-Arnold Networks for Medical Imaging
Youngjoon Lee, Jinu Gong, Joonhyuk Kang
Federated Learning (FL) enables model training across decentralized devices without sharing raw data, thereby preserving privacy in sensitive domains like healthcare. In this paper…