Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT)
have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI …

Recent developments in the Internet of Things (IoT) and various communication
technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into …

An increasing number of public datasets have shown a marked impact on automated organ
segmentation and tumor detection. However, due to the small size and partially labeled …

Abstract Model heterogeneous federated learning is a challenging task since each client
independently designs its own model. Due to the annotation difficulty and free-riding …

Federated learning is an emerging research paradigm enabling collaborative training of
machine learning models among different organizations while keeping data private at each …

Abstract Models trained in federated settings often suffer from degraded performances and
fail at generalizing, especially when facing heterogeneous scenarios. In this work, we …

We consider the problem of Federated Learning (FL) where numerous decentralized
computational nodes collaborate with each other to train a centralized machine learning …

Abstract Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple
hospitals to collaborate distributedly without aggregating local data, thereby protecting …

Healthcare is predominantly regarded as a crucial consideration in promoting the general
physical and mental health and well‐being of people around the world. The amount of data …

How to ensure fairness is an important topic in federated learning (FL). Recent studies have
investigated how to reward clients based on their contribution (collaboration fairness), and …