104 citations · 305 across the 23 of their papers we have counts for
5 papers · 1 filter
L-FGADMM: Layer-Wise Federated Group ADMM for Communication Efficient Decentralized Deep Learning
Anis Elgabli, Jihong Park, Sabbir Ahmed +1
This article proposes a communication-efficient decentralized deep learning algorithm, coined layer-wise federated group ADMM (L-FGADMM). To minimize an empirical risk, every worke…
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning
Anis Elgabli, Jihong Park, Amrit S. Bedi +3
In this article, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM). To reduce the number of communication li…
GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning
Anis Elgabli, Jihong Park, Amrit S. Bedi +2
When the data is distributed across multiple servers, lowering the communication cost between the servers (or workers) while solving the distributed learning problem is an importan…
Distilling On-Device Intelligence at the Network Edge
Jihong Park, Shiqiang Wang, Anis Elgabli +6
Devices at the edge of wireless networks are the last mile data sources for machine learning (ML). As opposed to traditional ready-made public datasets, these user-generated privat…
A Proximal Jacobian ADMM Approach for Fast Massive MIMO Signal Detection in Low-Latency Communications
Anis Elgabli, Ali Elghariani, Vaneet Aggarwal +2
One of the 5G promises is to provide Ultra Reliable Low Latency Communications (URLLC) which targets an end to end communication latency that is less than 1ms . The very low latenc…