10 papers
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…
Resource-Aware Aggregation and Sparsification in Heterogeneous Ensemble Federated Learning
Keumseo Ryum, Jinu Gong, Joonhyuk Kang
Federated learning (FL) enables distributed training with private client data, but its convergence is hindered by system heterogeneity under realistic communication scenarios. Most…
Energy-Efficient Secure Communications via Joint Optimization of UAV Trajectory and Movable-Antenna Array Beamforming
Sanghyeok Kim, Jinu Gong, Joonhyuk Kang
This paper investigates the potential of unmanned aerial vehicles (UAVs) equipped with movable-antenna (MA) arrays to strengthen security in wireless communication systems. We prop…
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…