2 citations · 3 across the 13 of their papers we have counts for
14 papers
Beyond Fixed Rounds: Data-Free Early Stopping for Practical Federated Learning
Youngjoon Lee, Hyukjoon Lee, Seungrok Jung +4
Federated Learning (FL) facilitates decentralized collaborative learning without transmitting raw data. However, reliance on fixed global rounds or validation data for hyperparamet…
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…
Revisit the Stability of Vanilla Federated Learning Under Diverse Conditions
Youngjoon Lee, Jinu Gong, Sun Choi +1
Federated Learning (FL) is a distributed machine learning paradigm enabling collaborative model training across decentralized clients while preserving data privacy. In this paper,…
Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation
Youngjoon Lee, Taehyun Park, Yunho Lee +2
Federated Learning (FL) is increasingly being adopted in military collaborations to develop Large Language Models (LLMs) while preserving data sovereignty. However, prompt injectio…
Embedding Byzantine Fault Tolerance into Federated Learning via Consistency Scoring
Youngjoon Lee, Jinu Gong, Joonhyuk Kang
Given sufficient data from multiple edge devices, federated learning (FL) enables training a shared model without transmitting private data to the central server. However, FL is ge…
Generative AI-Powered Plugin for Robust Federated Learning in Heterogeneous IoT Networks
Youngjoon Lee, Jinu Gong, Joonhyuk Kang
Federated learning enables edge devices to collaboratively train a global model while maintaining data privacy by keeping data localized. However, the Non-IID nature of data distri…
ROK Defense M&S in the Age of Hyperscale AI: Concepts, Challenges, and Future Directions
Youngjoon Lee, Taehyun Park, Yeongjoon Kang +2
Integrating hyperscale AI into national defense M&S(Modeling and Simulation), under the expanding IoMDT(Internet of Military Defense Things) framework, is crucial for boosting stra…
Fast-Convergent Federated Learning via Cyclic Aggregation
Youngjoon Lee, Sangwoo Park, Joonhyuk Kang
Federated learning (FL) aims at optimizing a shared global model over multiple edge devices without transmitting (private) data to the central server. While it is theoretically wel…
Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance
Youngjoon Lee, Sangwoo Park, Joonhyuk Kang
While being an effective framework of learning a shared model across multiple edge devices, federated learning (FL) is generally vulnerable to Byzantine attacks from adversarial ed…