3.6k citations · 29.1k across the 119 of their papers we have counts for
119 papers
Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx
Trung-Hieu Hoang, Jordan Fuhrman, Ravi Madduri +8
Facilitating large-scale, cross-institutional collaboration in biomedical machine learning projects requires a trustworthy and resilient federated learning (FL) environment to ensu…
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…
Star Cluster Classification using Deep Transfer Learning with PHANGS-HST
Stephen Hannon, Bradley C. Whitmore, Janice C. Lee +17
Currently available star cluster catalogues from HST imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. The time-consuming natur…
FAIR AI Models in High Energy Physics
Javier Duarte, Haoyang Li, Avik Roy +14
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientifi…
Numerical relativity higher order gravitational waveforms of eccentric, spinning, non-precessing binary black hole mergers
Abhishek V. Joshi, Shawn G. Rosofsky, Roland Haas +1
We use the open source, community-driven, numerical relativity software, the Einstein Toolkit to study the physics of eccentric, spinning, nonprecessing binary black hole mergers w…