Graph neural networks (GNNs) are a type of deep learning models that are trained on
graphs and have been successfully applied in various domains. Despite the effectiveness of …

We carefully evaluate a number of algorithms for learning in a federated environment, and
test their utility for a variety of image classification tasks. We consider many issues that have …

Asynchronous learning protocols have regained attention lately, especially in the Federated
Learning (FL) setup, where slower clients can severely impede the learning process. Herein …

Hierarchical federated learning (HFL) has demonstrated promising scalability advantages
over the traditional" star-topology" architecture-based federated learning (FL). However, HFL …

With the motive of training all the parameters of a neural network, we study why and when
one can achieve this by iteratively creating, training, and combining randomly selected …

The interest in federated learning has surged in recent research due to its unique ability to
train a global model using privacy-secured information held locally on each client. This …

Modern advancements in large-scale machine learning would be impossible without the
paradigm of data-parallel distributed computing. Since distributed computing with large …

Graph Neural Networks (GNNs) have demonstrated a great potential in a variety of graph-
based applications, such as recommender systems, drug discovery, and object recognition …

Optimizing for reduced computational and bandwidth resources enables model training in
less-than-ideal environments and paves the way for practical and accessible AI solutions …

We introduce a novel optimization problem formulation that departs from the conventional
way of minimizing machine learning model loss as a black-box function. Unlike traditional …