Practical Federated Learning Infrastructure for Privacy-Preserving Scientific Computing
DescriptionFederated learning (FL) is deemed a promising paradigm for privacy-preserving data analytics in collaborative scientific computing.
However, there lacks an effective and easy-to-use FL infrastructure for scientific computing and high-performance computing (HPC) environments. The objective of this presentation is two-fold. First, we identify three missing pieces of a scientific FL infrastructure:
(i) a native MPI programming interface that can be seamlessly integrated into existing scientific applications,
(ii) an independent data layer for the FL system such that the user can pick the persistent medium for her own choice, such as parallel file systems and multidimensional databases,
(iii) efficient encryption protocols that are optimized for scientific workflows.

The second objective is to present a work-in-progress FL infrastructure, namely MPI-FL, which is implemented with PyTorch and MPI4py. We deploy MPI-FL on 1,000 CPU cores and evaluate it with four standard benchmarks: MNIST, Fashion-MNIST, CIFAR-10, and SVHN-extra.
Event Type
Workshop
TimeMonday, 14 November 20223:45pm - 4pm CST
LocationC144-145
Session Formats
Recorded
Registration Categories
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