3 papers
cs.LG2026
Entropy-Regularized Probabilistic Gates for Sparse Model Discovery in Scarce-Data Federated Learning
Krishna Harsha Kovelakuntla Huthasana, Alireza Olama, Andreas Lundell
Federated Learning (FL) is a distributed machine learning (ML) paradigm with collaboration among multiple clients without sharing data. FL is challenging under data heterogeneity a…
stat.ML2025
Federated Learning With L0 Constraint Via Probabilistic Gates For Sparsity
Krishna Harsha Kovelakuntla Huthasana, Alireza Olama, Andreas Lundell
Federated Learning (FL) is a distributed machine learning setting that requires multiple clients to collaborate on training a model while maintaining data privacy. The unaddressed…
cs.DC2025
PruneX: A Hierarchical Communication-Efficient System for Distributed CNN Training with Structured Pruning
Alireza Olama, Andreas Lundell, Izzat El Hajj +2
Inter-node communication bandwidth increasingly constrains distributed training at scale on multi-node GPU clusters. While compact models are the ultimate deployment target, conven…