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 (FL) enables building robust and generalizable AI models by leveraging
diverse datasets from multiple collaborators without centralizing the data. We created …

Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …

Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly
benefited human society. Among various AI technologies, Federated Learning (FL) stands …

Over the past few years, Federated Learning (FL) has become an emerging machine
learning technique to tackle data privacy challenges through collaborative training. In the …

Privacy and Byzantine resilience are two indispensable requirements for a federated
learning (FL) system. Although there have been extensive studies on privacy and Byzantine …

Decentralised Machine Learning (DML) enables collaborative machine learning without
centralised input data. Federated Learning (FL) and Edge Inference are examples of DML …

Federated Learning (FL) is becoming popular in different industrial sectors where data
access is critical for security, privacy and the economic value of data itself. Unlike traditional …

Inspired by the successful application of dealing with graph-structured data, graph neural
networks (GNNs) have captured significant research attention. Considering the privacy …

To investigate the heterogeneity in federated learning in real-world scenarios, we generalize
the classic federated learning to federated hetero-task learning, which emphasizes the …