4 papers
Temperature Scaling Attack Disrupting Model Confidence in Federated Learning
Kichang Lee, Jaeho Jin, JaeYeon Park +2
Predictive confidence serves as a foundational control signal in mission-critical systems, directly governing risk-aware logic such as escalation, abstention, and conservative fall…
Toward Storage-Aware Learning with Compressed Data An Empirical Exploratory Study on JPEG
Kichang Lee, Songkuk Kim, JaeYeon Park +1
On-device machine learning is often constrained by limited storage, particularly in continuous data collection scenarios. This paper presents an empirical study on storage-aware le…
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…
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…