在金融信贷风险评估中,一个动态的受感场和改进的功能融合方法为联合学习提供了动态的受感场和改进的功能融合方法
Ruiheng Li1,2, Yue Cao1,3, Yuhang Shu1,3
1Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan, 430205, China.
Scientific reports
|November 3, 2024
概括
联合学习 (FL) 降低了金融违约风险分析的通信成本. 一个新的框架改进了特征提取和模型融合,为高效的私人人工智能减少了80%的通信回合.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 金融技术 金融技术
背景情况:
- 联合学习 (FL) 允许在没有数据共享的情况下进行分布式培训,从而增强隐私.
- 高通讯开销仍然是佛罗里达州的一个主要局限性,尤其是在非IID金融数据方面.
- 现有的FL方法在分析复杂的金融违约风险数据的计算成本方面扎.
研究的目的:
- 开发一个高效的联邦学习框架,用于评估金融信用风险.
- 为了解决非独立和相同分布 (非IID) 数据在FL中的挑战,用于金融应用.
- 为了减少通信开销,并提高FL中的模型准确性,以预测违约风险.
主要方法:
- 实现了用于自适应特征提取的动态受感场调整机制.
- 设计了一个分布式特征融合架构,用于汇总本地和全球模型特征.
- 利用多融合策略从本地和全球模型进行定制的特征聚合.
主要成果:
- 拟议的FL框架有效地使用动态受体场来优化卷积内核重量,以获得优异的特征提取.
- 多融合策略显著增强了本地和全球模型特征的聚合.
- 实验结果显示,与传统的FL相比,通信轮次减少了大约80%.
结论:
- 新型FL方法改善了用于金融违约风险分析的特征提取和模型融合.
- 动态接收场和先进的功能融合大大降低了FL的通信成本.
- 本研究提出了一种更有效,更准确的FL解决方案,用于敏感的财务数据分析.
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