Federated Learning (FL) is a machine learning technique that enables to collaboratively
learn valuable information across devices or sites without moving the data. In FL, the model …

Federated Learning (FL) is an AI framework that enables collaborative and distributed
training across multiple users to learn a global model while preserving the privacy of the …

The growing application of machine learning (ML) techniques in healthcare has led to
increased interest in federated learning (FL), which enables the secure and private training …

Federated learning (FL) has emerged as a promising solution to address the challenges
posed by data silos and the need for global data fusion. It offers a distributed machine …

Federated learning (FL) is a kind of distributed machine learning framework, where the
global model is generated on the centralized aggregation server based on the parameters of …

Technological advances in smart devices and applications targeting the Internet of
Healthcare Things provide a perfect environment for using Machine Learning-based …

The integration of healthcare and data-driven technologies offers remarkable opportunities
for medical research and patient care. However, it is crucial to adhere to the ethical …

Federated learning (FL) is a promising privacy-preserving solution to build powerful AI
models. In many FL scenarios, such as healthcare or smart city monitoring, the user's …

Federated learning (FL) is an efficient decentralized machine learning methodology for
processing nonindependent and identically distributed (non-IID) data due to geographical …

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 …