Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

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 has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Federated learning has emerged as an important paradigm for training machine learning
models in different domains. For graph-level tasks such as graph classification, graphs can …

Abstract Models trained in federated settings often suffer from degraded performances and
fail at generalizing, especially when facing heterogeneous scenarios. In this work, we …

Predicting traffic flow plays an important role in reducing traffic congestion and improving
transportation efficiency for smart cities. Traffic Flow Prediction (TFP) in the smart city …

Influenced by the great success of deep learning via cloud computing and the rapid
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …

Graph machine learning has gained great attention in both academia and industry recently.
Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are …

Federated learning has received increasing attention for its ability to collaborative learning
without leaking privacy. Promising advances have been achieved under the assumption that …

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of
the world's web traffic, making a great data source for various machine learning (ML) …