most citedE2GC: Energy-efficient Group Convolution in Deep Neural Networks

6 citations · 6 across the 2 of their papers we have counts for

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cs.LG2025

FedDPC : Handling Data Heterogeneity and Partial Client Participation in Federated Learning

Mrinmay Sen, Subhrajit Nag

Data heterogeneity is a significant challenge in modern federated learning (FL) as it creates variance in local model updates, causing the aggregated global model to shift away fro…

cs.LG2020★ 6 cited

E2GC: Energy-efficient Group Convolution in Deep Neural Networks

Nandan Kumar Jha, Rajat Saini, Subhrajit Nag +1

The number of groups () in group convolution (GConv) is selected to boost the predictive performance of deep neural networks (DNNs) in a compute and parameter efficient manner.…