Portrait de Zafir Khalid n'est pas disponible

Zafir Khalid

Maîtrise recherche - Concordia
Superviseur⋅e principal⋅e
Eugene Belilovsky
Co-supervisor
Irina Rish
Sujets de recherche
Apprentissage fédéré
Apprentissage profond
Vision par ordinateur
Site web
GitHub

Publications

Model Parallelism With Subnetwork Data Parallelism
Vaibhav Singh
Zafir Khalid
Edouard Oyallon
Eugene Belilovsky
Distributed pre-training of large models at scale often imposes heavy memory demands on individual nodes and incurs significant intra-node c… (voir plus)ommunication costs. We propose a novel alternative approach that reduces the memory requirements by training small, structured subnetworks of the model on separate workers. Unlike pipelining, our method avoids inter-node activation communication and maintains bandwidth requirements that are comparable to or lower than standard data parallel communication schemes based on all-reduce. We evaluate two subnetwork construction strategies guided by the principle of ensuring uniform representation of each parameter across the distributed training setup. Our results show that the stochastic block dropping technique consistently outperforms the width-wise subnetwork construction previously explored in federated learning. We empirically attribute this superior performance to stronger gradient alignment in subnetworks that retain blocks having skip connections. Preliminary experiments highlight the promise of our approach, achieving a
2025-06-10
ICML.cc/2025/Workshop/ES-FoMo-III (publié)
doi.org
openreview.net