HybridFL: A Federated Learning Approach for Financial Crime Detection
This addresses the challenge of financial crime detection where data is split across multiple parties, but it is incremental as it builds on existing federated learning methods.
The paper tackled the problem of federated learning with complex hybrid data distributions by proposing HybridFL, which integrates horizontal aggregation and vertical feature fusion, and demonstrated that it significantly outperforms transaction-only local models and achieves performance comparable to a centralized benchmark on AMLSim and SWIFT datasets.
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.