Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Recent developments in a variety of sectors, including health care, research and the direct-
to-consumer industry, have led to a dramatic increase in the amount of genomic data that …

In order to realize the full potential of wireless edge artificial intelligence (AI), very large and
diverse datasets will often be required for energy-demanding model training on resource …

Summary The Global Alliance for Genomics and Health (GA4GH) aims to accelerate
biomedical advances by enabling the responsible sharing of clinical and genomic data …

Cloud computing and cloud storage have contributed to a big shift in data processing and its
use. Availability and accessibility of resources with the reduction of substantial work is one of …

Abstract Model heterogeneous federated learning is a realistic and challenging problem.
However, due to the limitations of data collection, storage, and transmission conditions, as …

With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Medical data contains multiple records of patient data that are important for subsequent
treatment and future research. However, it needs to be stored and shared securely to protect …

Real-world healthcare data sharing is instrumental in constructing broader-based and larger
clinical datasets that may improve clinical decision-making research and outcomes …