1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2025
FRAIN to Train: A Fast-and-Reliable Solution for Decentralized Federated Learning
Sanghyeon Park, Soo-Mook Moon
Federated learning (FL) enables collaborative model training across distributed clients while preserving data locality. Although FedAvg pioneered synchronous rounds for global mode…
cs.LG2025★ 1 cited
CURing Large Models: Compression via CUR Decomposition
Sanghyeon Park, Soo-Mook Moon
Large deep learning models have achieved remarkable success but are resource-intensive, posing challenges such as memory usage. We introduce CURing, a novel model compression metho…
cs.CL2019★ 1 cited
Ethanos: Lightweight Bootstrapping for Ethereum
Jae-Yun Kim, Jun-Mo Lee, Yeon-Jae Koo +2
As ethereum blockchain has become popular, the number of users and transactions has skyrocketed, causing an explosive increase of its data size. As a result, ordinary clients using…