7 citations · 11 across the 8 of their papers we have counts for
4 papers · 1 filter
SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning
Riyasat Ohib, Bishal Thapaliya, Gintare Karolina Dziugaite +3
In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication. SSFL identifies a sparse subne…
SalientGrads: Sparse Models for Communication Efficient and Data Aware Distributed Federated Training
Riyasat Ohib, Bishal Thapaliya, Pratyush Gaggenapalli +3
Federated learning (FL) enables the training of a model leveraging decentralized data in client sites while preserving privacy by not collecting data. However, one of the significa…
Self-Supervised Mental Disorder Classifiers via Time Reversal
Zafar Iqbal, Usman Mahmood, Zening Fu +1
Data scarcity is a notable problem, especially in the medical domain, due to patient data laws. Therefore, efficient Pre-Training techniques could help in combating this problem. I…
Pipeline-Invariant Representation Learning for Neuroimaging
Xinhui Li, Alex Fedorov, Mrinal Mathur +4
Deep learning has been widely applied in neuroimaging, including predicting brain-phenotype relationships from magnetic resonance imaging (MRI) volumes. MRI data usually requires e…