16 citations · 25 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service
Zilinghan Li, Shilan He, Pranshu Chaturvedi +9
Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e…
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…
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…