Federated Learning (FL) obtained a lot of attention to the academic and industrial
stakeholders from the beginning of its invention. The eye-catching feature of FL is handling …

Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely,
the small sample size problem. Many recent studies suggest using multi-domain data …

Since its introduction in 2016, researchers have applied the idea of Federated Learning (FL)
to several domains ranging from edge computing to banking. The technique's inherent …

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 …

Federated learning has been extensively explored in privacy-preserving medical image
analysis. However, the domain shift widely existed in real-world scenarios still greatly limits …

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

Federated learning (FL) has received great attention in healthcare primarily due to its
decentralized, collaborative nature of building a machine learning (ML) model. Over the …

Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-
art performance in image classification and segmentation tasks, aiding disease diagnosis …

Federated Learning (FL) is a suitable solution for making use of sensitive data belonging to
patients, people, companies, or industries that are obligatory to work under rigid privacy …

Federated learning (FL) is an active area of research. One of the most suitable areas for
adopting FL is the medical domain, where patient privacy must be respected. Previous …