Technology Category
Machine Learning: Distributed Machine Learning & Federated LearningHumans and AI: Human-in-the-loop Machine LearningNatural Language Processing: Language Grounding & Multi-modal NLP
HybridFL: A Federated Learning Approach for Financial Crime Detection
🤖 AI Summary
This work addresses the challenge of financial crime detection under hybrid data partitioning, where data is simultaneously split across users (horizontally) and features (vertically). To tackle this, we propose HybridFL, a novel framework that seamlessly integrates horizontal federated learning with secure vertical feature fusion, enabling end-to-end privacy-preserving collaborative modeling between transacting parties and multiple banks without sharing raw data. HybridFL is designed for real-world heterogeneous data distributions and balances privacy and model performance through horizontal model aggregation and encrypted vertical feature interaction. Experiments on the AMLSim and SWIFT datasets demonstrate that HybridFL significantly outperforms baseline models trained solely on local transaction data and achieves performance close to that of centralized training.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
federated learning
financial crime detection
hybrid data partition
data locality
privacy-preserving machine learning
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hybrid Federated Learning
horizontal and vertical partitioning
financial crime detection
privacy-preserving machine learning
feature fusion
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