no code implementations • 1 Dec 2020 • K. R. Jayaram, Archit Verma, Ashish Verma, Gegi Thomas, Colin Sutcher-Shepard
Federated learning enables multiple, distributed participants (potentially on different clouds) to collaborate and train machine/deep learning models by sharing parameters/gradients.
1 code implementation • 17 Mar 2020 • Allison J. B. Chaney, Archit Verma, Young-suk Lee, Barbara E. Engelhardt
This uniquely allows NDMs both to deconvolve each observation into its constituent factors, and also to describe how the factor distributions specific to each observation vary across observations and deviate from the corresponding global factors.
no code implementations • 14 Sep 2019 • K. R. Jayaram, Vinod Muthusamy, Parijat Dube, Vatche Ishakian, Chen Wang, Benjamin Herta, Scott Boag, Diana Arroyo, Asser Tantawi, Archit Verma, Falk Pollok, Rania Khalaf
This paper describes the design, implementation and our experiences with FfDL, a DL platform used at IBM.