Artificial intelligence (AI) has led to a high rate of development in healthcare, and good
progress has been made on many complex medical problems. However, there is a lack of …

This research paper explores the transformative potential of federated learning in healthcare
informatics, focusing on its pivotal role in balancing advancements with privacy and security …

Federated learning (FL) provides a distributed machine learning system that enables
participants to train using local data to create a shared model by eliminating the requirement …

Today's artificial intelligence still faces two major challenges. One is that, in most industries,
data exists in the form of isolated islands. The other is the strengthening of data privacy and …

Recent medical applications are largely dominated by the application of Machine Learning
(ML) models to assist expert decisions, leading to disruptive innovations in radiology …

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 …

Federated learning methods offer secured monitor services and privacy-preserving
paradigms to end-users and organisations in the Internet of Things networks such as smart …

With the dramatically increasing deployment of IoT (Internet-of-Things) and communication,
data has always been a major priority to achieve intelligent healthcare in a smart city. For the …

Edge intelligent computing is widely used in the fields, such as the Internet of Medical
Things (IoMT), which has advantages, including high data processing efficiency, strong real …

Federated learning (FL) is a secure distributed machine learning paradigm that addresses
the issue of data silos in building a joint model. Its unique distributed training mode and the …