With the continuous increase in avenues of personal data generation, privacy protection has
become a hot research topic resulting in various proposed mechanisms to address this …

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

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 …

Driving style recognition of real-world drivers is beneficial for various reasons, such as safe
and economic driving, auto-insurance and designing autonomous systems. A common way …

In this article, we investigate the federated clustering (FedC) problem, which aims to
accurately partition unlabeled data samples distributed over massive clients into finite …

Federated clustering (FC) is an extension of centralized clustering in federated settings. The
key here is how to construct a global similarity measure without sharing private data, since …

Federated clustering (FC) is an emerging and important topic in data clustering research.
However, for existing works, there are two challenging issues as follows. 1) FC does not …

The use of medical data for machine learning, including unsupervised methods, such as
clustering, is often restricted by privacy regulations, such as the health insurance portability …

Federated learning is becoming increasingly popular to enable automated learning in
distributed networks of autonomous partners without sharing raw data. Many works focus on …

Federated Learning (FL) is an emerging distributed machine learning approach that
preserves client privacy by storing data on edge devices. However, data heterogeneity …