3 papers
cs.CR2026
ABC: Numerical Data Collection under Local Differential Privacy without Prior Knowledge
Incheol Baek, Hyungbin Kim, Yon Dohn Chung
Local Differential Privacy (LDP) provides strong privacy guarantees for collecting numerical data. A fundamental challenge, however, is that existing LDP mechanisms require a prede…
cs.LG2025
Federated Learning with Feedback Alignment
Incheol Baek, Hyungbin Kim, Minseo Kim +1
Federated Learning (FL) enables collaborative training across multiple clients while preserving data privacy, yet it struggles with data heterogeneity, where clients' data are not…
cs.LG2025
Decoupled Contrastive Learning for Federated Learning
Hyungbin Kim, Incheol Baek, Yon Dohn Chung
Federated learning is a distributed machine learning paradigm that allows multiple participants to train a shared model by exchanging model updates instead of their raw data. Howev…