31 citations · 65 across the 7 of their papers we have counts for
8 papers
The Switch from Conventional to SDN: The Case for Transport-Agnostic Congestion Control
Ahmed M. Abdelmoniem, Brahim Bensaou
To meet the timing requirements of interactive applications, the no-frills congestion-agnostic transport protocols like UDP are increasingly deployed side-by-side in the same netwo…
Rethinking gradient sparsification as total error minimization
Atal Narayan Sahu, Aritra Dutta, Ahmed M. Abdelmoniem +3
Gradient compression is a widely-established remedy to tackle the communication bottleneck in distributed training of large deep neural networks (DNNs). Under the error-feedback fr…
Implementation and Evaluation of Data Center Congestion Controller with Switch Assistance
Ahmed M. Abdelmoniem, Brahim Bensaou
In this work, we provide the design and implementation of a switch-assisted congestion control algorithm for data center networks (DCNs). In particular, we provide a prototype of t…
On the Impact of Device and Behavioral Heterogeneity in Federated Learning
Ahmed M. Abdelmoniem, Chen-Yu Ho, Pantelis Papageorgiou +2
Federated learning (FL) is becoming a popular paradigm for collaborative learning over distributed, private datasets owned by non-trusting entities. FL has seen successful deployme…
T-RACKs: A Faster Recovery Mechanism for TCP in Data Center Networks
Ahmed M. Abdelmoniem, Brahim Bensaou
Cloud interactive data-driven applications generate swarms of small TCP flows that compete for the small buffer space in data-center switches. Such applications require a short flo…
An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems
Ahmed M. Abdelmoniem, Ahmed Elzanaty, Mohamed-Slim Alouini +1
The recent many-fold increase in the size of deep neural networks makes efficient distributed training challenging. Many proposals exploit the compressibility of the gradients and…