Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications such as …

With the proliferation of Beyond 5G (B5G) communication systems and heterogeneous
networks, mobile broadband users are generating massive volumes of data that undergo …

As machine learning and artificial intelligence (ML/AI) are becoming more popular and
advanced, there is a wish to turn sensitive data into valuable information via ML/AI …

The energy industry is undergoing significant transformations as it strives to achieve net-
zero emissions and future-proof its infrastructure, where every participant in the power grid …

Advanced adversarial attacks such as membership inference and model memorization can
make federated learning (FL) vulnerable and potentially leak sensitive private data. Local …

Social recommendation has shown promising improvements over traditional systems since it
leverages social correlation data as an additional input. Most existing work assumes that all …

The Poisoning Membership Inference Attack (PMIA) is a newly emerging privacy attack that
poses a significant threat to federated learning (FL). An adversary conducts data poisoning …

Federated learning (FL) is a machine learning framework that enables the use of user data
for training without the need to share the data with the central server. FL's decentralized …

Many organizations own data but have limited machine learning expertise (data owners). On
the other hand, organizations that have expertise need data from diverse sources to train …

The increasing demand for privacy-preserving collaborative learning has given rise to a new
computing paradigm called federated learning (FL), in which clients collaboratively train a …