HybridFL: A Federated Learning Approach for Financial Crime Detection

TL;DR

HybridFL introduces a novel federated learning architecture that effectively combines horizontal and vertical data partitioning to enhance financial crime detection while preserving data privacy.

Contribution

The paper presents HybridFL, a new federated learning approach that handles complex hybrid data distributions across multiple parties for improved financial crime detection.

Findings

HybridFL outperforms local models in experiments.

HybridFL achieves near-centralized performance.

Effective privacy preservation in hybrid data settings.

Abstract

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.