5 papers
Spatial Discretization for Fine-Grain Zone Checks with STARKs
Sungmin Lee, Kichang Lee, Gyeongmin Han +1
Many location-based services rely on a point-in-polygon test (PiP), checking whether a point or a trajectory lies inside a geographic zone. Since geometric operations are expensive…
Verifiable Dropout: Turning Randomness into a Verifiable Claim
Kichang Lee, Sungmin Lee, Jaeho Jin +1
Modern cloud-based AI training relies on extensive telemetry and logs to ensure accountability. While these audit trails enable retrospective inspection, they struggle to address t…
FedUNet: A Lightweight Additive U-Net Module for Federated Learning with Heterogeneous Models
Beomseok Seo, Kichang Lee, JaeYeon Park
Federated learning (FL) enables decentralized model training without sharing local data. However, most existing methods assume identical model architectures across clients, limitin…
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…
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…