Presentation

· Contributors · Organizations · Search Program · My Schedule · Happening Now · Maps

This content is available for: Workshop Reg Pass. Upgrade Registration

The Second Workshop on Federated and Privacy Preserving AI for HPC

DescriptionFederated learning (FL) is a distributed machine learning method that allows a network of participants to collaboratively train a shared model without exchanging their data. Since this is a quite new and fast evolving scientific domain, there are myriads of outstanding algorithmic, technical, administrative, and policy issues with the deployment and use of federated learning across different compute environments. The goal of the First Workshop on Federated and Privacy Preserving AI for HPC (FPPAI4HPC) was to start building the community by discussing different aspects of federated and privacy preserving AI as well as the potential benefits and challenges of developing and deploying FL and other privacy preserving methods for AI applications on HPC platforms. The goal of the second FPPAI4HPC workshop is to continue building the community by taking a more focused approach to early experiences and best practices in developing and deploying FPPAI frameworks on different computing platforms.

Organizers

Georgia Tourassi

Oak Ridge National Laboratory

Eric Lancon

Brookhaven National Laboratory

Irina Rish

University of Montreal

Mila – Quebec AI Institute