Federated Learning (FL), a novel distributed interactive AI paradigm, holds particular
promise for smart healthcare since it enables many clients including hospitals to take part in …

Vast developments in technology have occurred in both hardware and software, and now
increasing amounts of data related to healthcare is readily available, for example, the data of …

Federated learning (FL), also referred to as collaborative learning, uses a number of
dispersed edge devices or servers to run the training algorithms, without exchanging local …

Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …

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

Federated learning (FL) has emerged as a promising approach for training machine
learning models on distributed data while preserving privacy specifically in the field of …

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 …

Objectives Federated learning (FL) allows multiple institutions to collaboratively develop a
machine learning algorithm without sharing their data. Organizations instead share model …

In recent times, a lot of development has taken place in deep learning, as a result of which
development in smart healthcare applications has increased to a great extent. It successfully …

In the contemporary landscape, machine learning has a pervasive impact across virtually all
industries. However, the success of these systems hinges on the accessibility of training …