With the rapid development of computer software and hardware technologies, more and
more healthcare data are becoming readily available from clinical institutions, patients …

Data privacy is a very important issue. Especially in fields like medicine, it is paramount to
abide by the existing privacy regulations to preserve patients' anonymity. However, data is …

While data in healthcare is produced in quantities never imagined before, the feasibility of
clinical studies is often hindered by the problem of data access and transfer, especially …

Recent advances in deep learning have shown many successful stories in smart healthcare
applications with data-driven insight into improving clinical institutions' quality of care …

In the medical or healthcare industry, where, the already available information or data is
never sufficient, excellence can be performed with the help of Federated Learning (FL) by …

The use of machine learning (ML) with electronic health records (EHR) is growing in
popularity as a means to extract knowledge that can improve the decision-making process in …

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis
on enabling software and hardware platforms, protocols, real-life applications and use …

Privacy protection is paramount in conducting health research. However, studies often rely
on data stored in a centralized repository, where analysis is done with full access to the …

Background In an era of “big data,” computationally efficient and privacy-aware solutions for
large-scale machine learning problems become crucial, especially in the healthcare …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …