2 citations · 4 across the 5 of their papers we have counts for
5 papers
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…
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…
A Blockchain-based Platform for Reliable Inference and Training of Large-Scale Models
Sanghyeon Park, Junmo Lee, Soo-Mook Moon
As artificial intelligence (AI) continues to permeate various domains, concerns surrounding trust and transparency in AI-driven inference and training processes have emerged, parti…
RouTEE: A Secure Payment Network Routing Hub using Trusted Execution Environments
Junmo Lee, Seongjun Kim, Sanghyeon Park +1
Cryptocurrencies such as Bitcoin and Ethereum have made payment transactions possible without a trusted third party, but they have a scalability issue due to their consensus mechan…
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…