4 papers
From Pilots to Production: Lessons in Cross-Institutional Federated Training and Artificial Intelligence for Science
Olivera Kotevska, Max Carlson, Yan Gao +10
Many of the most valuable scientific datasets cannot be centralized: they are proprietary, export-controlled, classified, or bound by data-sovereignty restrictions. This inverts th…
Tula: Optimizing Time, Cost, and Generalization in Distributed Large-Batch Training
Sahil Tyagi, Feiyi Wang
Distributed training increases the number of batches processed per iteration either by scaling-out (adding more nodes) or scaling-up (increasing the batch-size). However, the large…
OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC
Sahil Tyagi, Andrei Cozma, Olivera Kotevska +1
Federated Learning (FL) is critical for edge and High Performance Computing (HPC) where data is not centralized and privacy is crucial. We present OmniFed, a modular framework desi…
On Using Large-Batches in Federated Learning
Sahil Tyagi
Efficient Federated learning (FL) is crucial for training deep networks over devices with limited compute resources and bounded networks. With the advent of big data, devices eithe…