2 citations · 3 across the 15 of their papers we have counts for
5 papers · 1 filter
Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning
Kichang Lee, Jaeho Jin, JaeYeon Park +2
Federated learning enables decentralized model training without sharing raw data, preserving data privacy. However, its vulnerability towards critical security threats, such as gra…
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning
Kichang Lee, Yujin Shin, Jonghyuk Yun +3
Federated Learning (FL) enables collaborative model training across distributed devices while preserving local data privacy, making it ideal for mobile and embedded systems. Howeve…
Analyzing the Impact of the Automatic Ball Strike System in Professional Baseball through a Case Study on KBO League Data
Kichang Lee, Kyungsik Han, JeongGil Ko
Recent advancements in professional baseball have led to the introduction of the Automated Ball-Strike (ABS) system, or ``robot umpires,'' which utilize machine learning, computer…
Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generation
Yujin Shin, Kichang Lee, Sungmin Lee +3
While federated learning leverages distributed client resources, it faces challenges due to heterogeneous client capabilities. This necessitates allocating models suited to clients…
Improving Local Training in Federated Learning via Temperature Scaling
Kichang Lee, Pei Zhang, Songkuk Kim +1
Federated learning is inherently hampered by data heterogeneity: non-i.i.d. training data over local clients. We propose a novel model training approach for federated learning, FLe…