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cs.LG2026
Pushing the (Decision) Boundaries: Dynamically Calibrating Differentially Private Noise to Explainability in Federated Learning
Michael Khavkin, Kichang Lee, Jaeho Jin +2
Federated Learning (FL) with Differential Privacy (DP) is increasingly adopted to preserve data confidentiality in distributed machine learning. However, DP noise distorts learned…
cs.LG2026
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…
cs.LG2024
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…