联邦学习与其在不同产业中的应用概述

Federal Learning and Its Application in Different Industries

联邦学习(Federated Learning)是机器学习的一种新的范式,它可以跨设备进行分布式学习,以消除“数据孤岛”问题,同时保护模型在学习过程中的数据交互隐私。文中首先对联邦学习的兴起和发展进行概述;其次归纳了联邦学习的定义及算法;然后重点介绍了联邦学习在医疗、金融等行业中的应用,探讨了联邦学习现在面临的挑战;最后展望了联邦学习在当今时代的发展前景。

Federated Lerning is a new paradigm of machine learning,it can conduct distributed learning across devices to eliminate the problem of"data islands"and protect the privacy of data interaction during model learning.Firstly,this paper summarizes the rise and development of federal learning;secondly,the definition and algorithm of federated learning are summarized,then it focuses on the implementation and application of federal learning in medical,financial and other industries,and discusses the challenges faced by federal learning;finally,the paper looks forward to the feasible development prospects of federal learning in the current era.