117 citations · 195 across the 4 of their papers we have counts for
4 papers
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…
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning
Aritra Dutta, El Houcine Bergou, Ahmed M. Abdelmoniem +4
Compressed communication, in the form of sparsification or quantization of stochastic gradients, is employed to reduce communication costs in distributed data-parallel training of…
Natural Compression for Distributed Deep Learning
Samuel Horvath, Chen-Yu Ho, Ludovit Horvath +3
Modern deep learning models are often trained in parallel over a collection of distributed machines to reduce training time. In such settings, communication of model updates among…
Scaling Distributed Machine Learning with In-Network Aggregation
Amedeo Sapio, Marco Canini, Chen-Yu Ho +7
Training machine learning models in parallel is an increasingly important workload. We accelerate distributed parallel training by designing a communication primitive that uses a p…