4 papers
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…
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…
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…
Adaptive Factor Graph-Based Tightly Coupled GNSS/IMU Fusion for Robust Positionin
Elham Ahmadi, Alireza Olama, Petri Välisuo +1
Reliable positioning in GNSS-challenged environments remains a critical challenge for navigation systems. Tightly coupled GNSS/IMU fusion improves robustness but remains vulnerable…