Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical
management compared to other types of cancer. Precision risk stratification and tailored …

The Internet of Medical Things (IoMT) has a bright future with the development of smart
mobile devices. Information technology is also leading changes in the healthcare industry …

Clustered federated learning (FL) has been shown to produce promising results by grouping
clients into clusters. This is especially effective in scenarios where separate groups of clients …

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) …

In recent years, the Internet of Vehicles (IoV) has garnered significant attention from
researchers and automotive industry professionals due to its expanding range of …

Federated learning is a distributed learning paradigm where multiple agents, each only with
access to local data, jointly learn a global model. There has recently been an explosion of …

One of the key challenges in federated learning (FL) is local data distribution heterogeneity
across clients, which may cause inconsistent feature spaces across clients. To address this …

Federated learning (FL) is a promising framework for privacy-preserving and distributed
training with decentralized clients. However, there exists a large divergence between the …

We propose flexible vertical federated learning (Flex-VFL), a distributed machine algorithm
that trains a smooth, nonconvex function in a distributed system with vertically partitioned …

The growing interest in intelligent services and privacy protection for mobile devices has
given rise to the widespread application of federated learning in Multi-access Edge …