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[2602.19207] HybridFL: A Federated Learning Approach for Financial Crime Detection
Summary
The paper presents HybridFL, a federated learning approach designed for financial crime detection, which integrates horizontal and vertical data aggregation to enhance model performance while preserving data privacy.
Why It Matters
As financial institutions increasingly face challenges in detecting crimes while maintaining data privacy, HybridFL offers a novel solution that combines different data partitioning strategies. This approach not only improves detection capabilities but also adheres to privacy regulations, making it relevant for banks and regulatory bodies.
Key Takeaways
- HybridFL integrates horizontal and vertical data aggregation for improved model performance.
- The approach preserves data locality, addressing privacy concerns in financial crime detection.
- Experiments show HybridFL outperforms local models and matches centralized benchmarks.
- The method is particularly applicable in scenarios with disjoint user data and complementary features.
- This research highlights the potential of federated learning in sensitive domains like finance.
Computer Science > Machine Learning arXiv:2602.19207 (cs) [Submitted on 22 Feb 2026] Title:HybridFL: A Federated Learning Approach for Financial Crime Detection Authors:Afsana Khan, Marijn ten Thij, Guangzhi Tang, Anna Wilbik View a PDF of the paper titled HybridFL: A Federated Learning Approach for Financial Crime Detection, by Afsana Khan and 3 other authors View PDF HTML (experimental) 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. Subjects: Machine Learning (cs.LG); Artificial Intellig...
