论文标题
对联邦学习的全面调查：概念和应用
A Comprehensive Survey on Federated Learning: Concept and Applications
论文作者
论文摘要
本文提供了联合学习（FL）的全面研究，重点是组件，挑战，应用和FL环境。 FL可以适用于现实生活模型中的多个字段和域。在医疗系统中，患者记录及其医疗状况的隐私是关键数据，因此合作学习或联合学习。另一方面，建立一个智能系统可以协助医务人员，而无需将数据引线分享到FL概念中，并且使用的应用程序是一种基于AI方法的脑肿瘤诊断智能系统，该系统可以在协作环境中有效地工作。本文将在FL概念中介绍一些在医疗领域的应用和相关工作，然后在FL概念中介绍他们的主要限制。
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.
