Federated learning (FL) has enabled training models collaboratively from multiple data
owning parties without sharing their data. Given the privacy regulations of patient's …

Nowadays, machine learning affects practically every industry, but the effectiveness of these
systems depends on the accessibility of training data sets. Every device now produces data …

Data privacy has become a major concern in healthcare due to the increasing digitization of
medical records and data-driven medical research. Protecting sensitive patient information …

The adoption of the advanced data analytics methods has been limited in industries
governed by strict data reuse regulations, such as healthcare. Barriers to data access and …

Federated Learning (FL) is concept that has been adopted in medical field to analyze data in
individual devices through aggregation of machine learning model in global server. It also …

Federated Learning (FL) is a Distributed Machine Learning model that has been used in
many applications today. It has been pioneered by companies like Google Inc and Apple Inc …

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 …

Advances have been made in the field of Machine Learning showing that it is an effective
tool that can be used for solving real world problems. This success is hugely attributed to the …

As the COVID19 pandemic began spreading, there were only pockets of information
available with hospitals across geographies. Researchers attempting to analyze information …

This paper presents the use of federated learning (FL) in healthcare to improve the efficiency
and accuracy of medical diagnosis while addressing privacy concerns related to medical …