Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon
decentralized data and training that brings learning to the edge or directly on-device. FL is a …

Federated learning preserves the privacy of user data through Machine Learning (ML). It
enables the training of an ML model during this process. The Healthcare Internet of Things …

Federated learning is a new type of distributed learning framework that allows multiple
participants to share training results without revealing their data privacy. As data privacy …

The field of machine learning has been seen as a major development in the last few years.
Many new algorithms and many new methods have been put forward by various …

Nowadays, Federated Learning (FL) over Internet of Medical Things (IoMT) devices has
become a current research hotspot. As a new architecture, FL can well protect the data …

Federated learning (FL) is known to perform machine learning tasks in a distributed manner.
Over the years, this has become an emerging technology, especially with various data …

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 …

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

With the rapid development of the Internet of Medical Things (IoMT), medical institutions are
accumulating vast amounts of medical data and aiming to utilize this data to train high …

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 …