2 citations · 5 across the 11 of their papers we have counts for
5 papers · 1 filter
Compressed Active Subspaces for Scalable Bayesian Inference
Thomas Flynn, Sanket Jantre, Byung-Jun Yoon +1
Active subspace methods provide a framework for quantifying predictive uncertainty in high-dimensional models by identifying and performing inference along parameter directions tha…
Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputers
Yijiang Li, Zilinghan Li, Kyle Chard +4
Artificial Intelligence for scientific applications increasingly requires training large models on data that cannot be centralized due to privacy constraints, data sovereignty, or…
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…
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler
Zilinghan Li, Pranshu Chaturvedi, Shilan He +6
Cross-silo federated learning offers a promising solution to collaboratively train robust and generalized AI models without compromising the privacy of local datasets, e.g., health…
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…