Although federated learning has increasingly gained attention in terms of effectively utilizing
local devices for data privacy enhancement, recent studies show that publicly shared …

As real-world graphs expand in size, larger GNN models with billions of parameters are
deployed. High parameter count in such models makes training and inference on graphs …

Performing training and inference for Graph Neural Networks (GNNs) under tight latency
constraints has become increasingly difficult as real-world input graphs continue to grow …

Filter pruning is the most representative technique for lightweighting convolutional neural
networks (CNNs). In general, filter pruning consists of the pruning and fine-tuning phases …

The proliferation of distributed energy resources (DERs), located at the Distribution System
Operator (DSO) level, bring new opportunities as well as new challenges to the operations …

In this work, we consider the problem of designing secure and efficient federated learning
(FL) frameworks. Existing solutions either involve a trusted aggregator or require …

Being able to learn from complex data with phase information is imperative for many signal
processing applications. Today's real-valued deep neural networks (DNNs) have shown …

We present ECToNAS, a cost-efficient evolutionary cross-topology neural architecture
search algorithm that does not require any pre-trained meta controllers. Our framework is …

Highlights A rice-leaf-disease detection and classification algorithm for multiple rice-leaf-
diseases in a complicated rice leaf image is proposed in this article. To increase rice-leaf …

Intra-Cardiac Electrogram (IEGM) is widely used to identify life-threatening ventricular
arrhythmias in medical devices to prevent sudden cardiac death, eg, Implantable …