金融领域中的联邦学习研究  

Research on federated learning in the field of finance

作　　者：郑润达 张维[1] 张永杰[1] 杨春 Zheng Runda;Zhang Wei;Zhang Yongjie;Yang Chun(College of Management and Economics,Tianjin University,Tianjin 300072,China;GienTech,Beijing 100192,China)

机构地区：[1]天津大学管理与经济学部,天津300072 [2]中电金信软件有限公司,北京100192

出　　处：《数智技术研究与应用》2025年第1期1-17,共17页SmartTech Innovations

基　　金：国家自然科学基金项目(72342022、72271184);国家重点研发计划课题(2023YFC3305304)。

摘　　要：在数字经济时代,数据挖掘方法和人工智能技术逐渐得到发展和应用,数据正在成为一项重要的资产,为企业经营和社会发展带来巨大价值。为了能够有效利用大数据产生实际效益,不同来源数据之间的联合和共享成为一种必然趋势。与此同时,数据安全和隐私保护也成为当前的实际需求和亟待解决的问题,联邦学习的提出为解决这类问题提供了可行的方案。该文聚焦联邦学习技术在金融领域中的应用场景和学术研究,按照联邦学习的类别对有价值的应用场景和相关研究进行梳理和总结,并在学术研究方面提出一些关键的科学问题,为之后的相关研究提供借鉴和参考。In the era of digital economy,data mining methods and artificial intelligence technology have been gradually developed and applied.Data is becoming an important asset,which brings great value to enterprise management and social development.In order to effectively use big data to produce practical benefits,the association and sharing of data from different sources has become an inevitable trend.At the same time,data security and privacy protection have also become the current practical needs and urgent problems to be solved.The proposal of federated learning provides a feasible solution to solve these problems.This paper focuses on the application scenarios and academic research of federated learning technology in the financial field,combs and summarizes the valuable application scenarios and related research according to the category of federated learning,and puts forward some key scientific issues in academic research,so as to provide reference for subsequent related research.

关 键 词：联邦学习 数据共享 隐私保护 金融领域 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]