16 citations · 43 across the 18 of their papers we have counts for
19 papers
From Pilots to Production: Lessons in Cross-Institutional Federated Training and Artificial Intelligence for Science
Olivera Kotevska, Max Carlson, Yan Gao +10
Many of the most valuable scientific datasets cannot be centralized: they are proprietary, export-controlled, classified, or bound by data-sovereignty restrictions. This inverts th…
Federated Low-Rank Tensor Estimation for Multimodal Image Reconstruction
Anh Van Nguyen, Diego Klabjan, Minseok Ryu +2
Low-rank tensor estimation offers a powerful approach to addressing high-dimensional data challenges and can substantially improve solutions to ill-posed inverse problems, such as…
A GPU-Accelerated Distributed Algorithm for Optimal Power Flow in Distribution Systems
Minseok Ryu, Geunyeong Byeon, Kibaek Kim
We propose a GPU-accelerated distributed optimization algorithm for controlling multi-phase optimal power flow in active distribution systems with dynamically changing topologies.…
FIRM: Federated Image Reconstruction using Multimodal Tomographic Data
Geunyeong Byeon, Minseok Ryu, Zichao Wendy Di +1
We propose a federated algorithm for reconstructing images using multimodal tomographic data sourced from dispersed locations, addressing the challenges of traditional unimodal app…
Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System
Ravi Madduri, Zilinghan Li, Tarak Nandi +3
The concept of a learning healthcare system (LHS) envisions a self-improving network where multimodal data from patient care are continuously analyzed to enhance future healthcare…
Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework
Zilinghan Li, Shilan He, Ze Yang +3
Federated learning (FL) is a distributed machine learning paradigm enabling collaborative model training while preserving data privacy. In today's landscape, where most data is pro…