Graph neural networks (GNNs) have attracted extensive research attention in recent years
due to their capability to progress with graph data and have been widely used in practical …

Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

Federated learning (FL) research has made progress in developing algorithms for
distributed learning of global models, as well as algorithms for local personalization of those …

This review article looks at recent research on Federated Learning (FL) for Collaborative
Intrusion Detection Systems (CIDS) to establish a taxonomy and survey. The motivation …

Cross-training has become a promising strategy to handle data heterogeneity problem in
federated learning, which re-train a local model across different clients to improve its …

Foreseeing the evolution of brain connectivity between anatomical regions from a baseline
observation can propel early disease diagnosis and clinical decision making. Such task …

This study explores the benefits of integrating the novel clustered federated learning (CFL)
approach with non-orthogonal multiple access (NOMA) under non-independent and …

Federated learning is a distributed machine learning approach in which a single server and
multiple clients collaboratively build machine learning models without sharing datasets on …

Federated learning is a distributed machine learning approach that allows a single server to
collaboratively build machine learning models with multiple clients without sharing datasets …

Although Federated Learning (FL) has garnered increasing attention from researchers, the
development of ad-vanced FL frameworks incorporating multiple access techniques remains …