In healthcare, machine learning (ML) shows significant potential to augment patient care,
improve population health, and streamline healthcare workflows. Realizing its full potential …

This paper focuses on the problem of personalized federated learning (FL) with the schema
of contrastive learning (CL), which is to implement collaborative pattern classification by …

Federated learning, which allows distributed medical institutions to train a shared deep
learning model with privacy protection, has become increasingly popular recently. However …

Multi-view clustering based on non-negative matrix factorization (NMFMvC) is a well-known
method for handling high-dimensional multi-view data. To satisfy the non-negativity …

Recently, federated multi-view clustering (FedMVC) has emerged as a powerful tool to
uncover complementary cluster structures across distributed clients, gaining significant …

Driven by the deep learning (DL) revolution, artificial intelligence (AI) has become a
fundamental tool for many biomedical tasks, including analyzing and classifying diagnostic …

Multiview clustering has been received considerable attention due to the widespread
collection of multiview data from diverse domains and sources. However, storing multiview …

Federated multi-view clustering has the potential to learn a global clustering model from
data distributed across multiple devices. In this setting, label information is unknown and …

Machine learning (ML) in healthcare presents numerous opportunities for enhancing patient
care, population health, and healthcare providers' workflows. However, the real-world …

Federated learning (FL) ameliorates privacy concerns in settings where a central server
coordinates learning from data distributed across many clients; rather than sharing the data …