14 citations · 23 across the 6 of their papers we have counts for
9 papers
APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning
Minseok Ryu, Youngdae Kim, Kibaek Kim +1
Federated learning (FL) enables training models at different sites and updating the weights from the training instead of transferring data to a central location and training as in…
Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates
Minseok Ryu, Kibaek Kim
Differential privacy (DP) techniques can be applied to the federated learning model to statistically guarantee data privacy against inference attacks to communication among the lea…
Accelerated Computation and Tracking of AC Optimal Power Flow Solutions using GPUs
Youngdae Kim, Kibaek Kim
We present a scalable solution method based on an alternating direction method of multipliers and graphics processing units (GPUs) for rapidly computing and tracking a solution of…
Numerical Performance of Different Formulations for Alternating Current Optimal Power Flow
Sayed Abdullah Sadat, Kibaek Kim
Alternating current optimal power flow (ACOPF) problems are nonconvex and nonlinear optimization problems. Utilities and independent service operators (ISO) require ACOPF to be sol…
Leveraging GPU batching for scalable nonlinear programming through massive Lagrangian decomposition
Youngdae Kim, François Pacaud, Kibaek Kim +1
We present the implementation of a trust-region Newton algorithm ExaTron for bound-constrained nonlinear programming problems, fully running on multiple GPUs. Without data transfer…
Differentially Private Federated Learning via Inexact ADMM
Minseok Ryu, Kibaek Kim
Differential privacy (DP) techniques can be applied to the federated learning model to protect data privacy against inference attacks to communication among the learning agents. Th…