The joint implementation of Federated learning (FL) and Explainable artificial intelligence
(XAI) will allow training models from distributed data and explaining their inner workings …

This paper explores constrained non-convex personalized federated learning (PFL), in
which a group of workers train local models and a global model, under the coordination of a …

The emerging federated learning applications raise challenges of Byzantine-robustness and
communication efficiency in distributed non-convex learning over non-IID data. To address …

Communication errors caused by noisy links can negatively impact the accuracy of
federated learning (FL) algorithms. To address this issue, we introduce an FL algorithm that …

Federated graph learning (FGL) enables the collaborative training of graph neural networks
(GNNs) in a distributed manner. A critical challenge in FGL is label deficiency, which …

As a new federated learning (FL) paradigm, clustered federated learning (CFL) could
effectively address the issue of model training accuracy loss due to different data distribution …