3 papers
cs.LG2026
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…
cs.LG2025
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…
cs.LG2025
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…