混合联邦学习提升金融犯罪检测精度，保护数据隐私。
HybridFL: A Federated Learning Approach for Financial Crime Detection
- 融合横向与纵向联邦学习，联合训练跨用户和跨特征模型。
- 在AMLSim和SWIFT数据集上优于本地模型，接近中心化基准表现。
- 适合银行等机构联合建模，兼顾隐私与性能。
联邦学习（FL）是一种保护隐私的机器学习范式，允许多方在不共享原始数据的情况下协作训练模型。传统联邦学习通常处理水平或垂直数据划分，但现实场景常呈现复杂的混合分布。本文提出混合联邦学习（HybridFL），解决用户间水平划分与特征间垂直划分并存的问题。在金融犯罪检测场景中，交易方持有交易级属性，多家银行维护私有的账户级特征。通过整合水平聚合与垂直特征融合，该架构实现联合学习同时严格保持数据本地性。在AMLSim和SWIFT数据集上的实验表明，HybridFL显著优于仅使用交易信息的本地模型，且性能接近集中式基准。
原文摘要 · Abstract (English)
Federated learning (FL) is a privacy-preserving machine learning paradigm that enables multiple parties to collaboratively train models on privately owned data without sharing raw information. While standard FL typically addresses either horizontal or vertical data partitions, many real-world scenarios exhibit a complex hybrid distribution. This paper proposes Hybrid Federated Learning (HybridFL) to address data split both horizontally across disjoint users and vertically across complementary feature sets. We evaluate HybridFL in a financial crime detection context, where a transaction party holds transaction-level attributes and multiple banks maintain private account-level features. By integrating horizontal aggregation and vertical feature fusion, the proposed architecture enables joint learning while strictly preserving data locality. Experiments on AMLSim and SWIFT datasets demonstrate that HybridFL significantly outperforms the transaction-only local model and achieves performance comparable to a centralized benchmark.
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