中圖分類號： TP391
文獻標(biāo)識碼： A
DOI： 10.19358/j.issn.2096-5133.2022.01.001
引用格式： 張海濤. 聯(lián)邦學(xué)習(xí)在金融數(shù)據(jù)安全領(lǐng)域的研究與應(yīng)用[J].信息技術(shù)與網(wǎng)絡(luò)安全，2022，41(1)：3-9.

Research and application of federated learning in the field of financial data security

Abstract： Abstract： In recent years, the problem of data leakage caused by the circulation of plaintext data in the financial field has become increasingly prominent. The traditional machine learning method of inter agency data fusion faces new problems and challenges. Therefore, based on the field of financial data security, from the perspective of user privacy and data security, this paper summarizes the federated learning theory, deeply analyzes its current application status in the financial industry, and points out that the existing federated learning still has some problems, such as low communication efficiency and prominent data heterogeneity. Finally,it puts forward suggestions on improving the federated learning standard system and paying attention to regulatory requirements at all times, so as to provide reference opinions for promoting the legal application of federated learning in the field of financial data security.

Key words : federated learning；financial data security；data privacy；credit card fraud