Federated learning (FL) is a system in which a central aggregator coordinates the efforts of multiple clients to solve machine learning problems. This setting allows training data to be dispersed in order to protect privacy. The purpose of this paper is to provide an overview of FL systems with a focus on healthcare. FL is evaluated here based on its frameworks, architectures, and applications. It is shown here that FL solves the preceding issues with a shared global deep learning (DL) model via a central aggregator server. This paper examines recent developments and provides a comprehensive list of unresolved issues, inspired by the rapid growth of FL research. In the context of FL, several privacy methods are described, including secure multiparty computation, homomorphic encryption, differential privacy, and stochastic gradient descent. Furthermore, a review of various FL classes, such as horizontal and vertical FL and federated transfer learning, is provided. FL has applications in wireless communication, service recommendation, intelligent medical diagnosis systems, and healthcare, all of which are discussed in this paper. We also present a thorough review of existing FL challenges, such as privacy protection, communication cost, system heterogeneity, and unreliable model upload, followed by future research directions.
翻译:联邦学习(FL)是一个系统,中央集成器在其中协调多个客户解决机器学习问题的努力,这种集成器可以分散培训数据,以保护隐私。本文件的目的是概述FL系统,重点是医疗保健。这里根据FL的框架、结构和应用对FL进行了评估。这里显示FL通过中央集成器服务器,用一个全球深度共同学习(DL)模式解决上述问题。本文件审查最近的发展情况,并提供了一份综合的未解决的问题清单,这是受FL研究的迅速发展所启发的。在FL方面,我们描述了几种隐私方法,包括安全的多式计算、同色加密、不同隐私和相近梯度脱落。此外,还提供了对各种FL课程的审查,如横向和纵向FL和垂直的FL以及联动转移学习。FL在无线通信、服务建议、智能医疗诊断系统和保健方面都有应用,本文讨论了所有这些应用。我们还对现有的FL挑战进行了透彻的审查,例如隐私保护、通信成本、差异性、未来研究方向、安全性、上传系统。