A Comprehensive Survey on Federated Learning: Concept and Applications
It offers a systematic overview of FL concepts and applications, primarily for researchers and practitioners in fields like healthcare where data privacy is critical, but it is incremental as a survey paper.
This paper provides a comprehensive survey of Federated Learning (FL), examining its components, challenges, applications, and environments, with a focus on medical applications such as brain tumor diagnosis systems.
This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on components, challenges, applications and FL environment. FL can be applicable in multiple fields and domains in real-life models. in the medical system, the privacy of patients records and their medical condition is critical data, therefore collaborative learning or federated learning comes into the picture. On other hand build an intelligent system assist the medical staff without sharing the data lead into the FL concept and one of the applications that are used is a brain tumor diagnosis intelligent system based on AI methods that can efficiently work in a collaborative environment.this paper will introduce some of the applications and related work in the medical field and work under the FL concept then summarize them to introduce the main limitations of their work.