2 citations · 6 across the 10 of their papers we have counts for
10 papers
Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers
Shourya Bose, Yu Zhang, Kibaek Kim
The advent of smart meters has enabled pervasive collection of energy consumption data for training short-term load forecasting models. In response to privacy concerns, federated l…
Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2
Zilinghan Li, Shilan He, Pranshu Chaturvedi +4
Federated learning enables multiple data owners to collaboratively train robust machine learning models without transferring large or sensitive local datasets by only sharing the p…
On Solving Unit Commitment with Alternating Current Optimal Power Flow on GPU
Weiqi Zhang, Youngdae Kim, Kibaek Kim
We consider the unit commitment (UC) problem that employs the alternating current optimal power flow (ACOPF) constraints, which is formulated as a mixed-integer nonlinear programmi…
GPU-Accelerated Sequential Quadratic Programming Algorithm for Solving ACOPF
Bowen Li, Michel Schanen, Kibaek Kim
Sequential quadratic programming (SQP) is widely used in solving nonlinear optimization problem, with advantages of warm-starting solutions, as well as finding high-accurate soluti…
A GPU-based Distributed Algorithm for Linearized Optimal Power Flow in Distribution Systems
Minseok Ryu, Geunyeong Byeon, Kibaek Kim
We propose a GPU-based distributed optimization algorithm, aimed at controlling optimal power flow in multi-phase and unbalanced distribution systems. Typically, conventional distr…
Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients
Shourya Bose, Kibaek Kim
The advent of smart meters has enabled pervasive collection of energy consumption data for training short-term load forecasting (STLF) models. In response to privacy concerns, fede…