A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection

2023-02-07 · Haobo Zhang, Junyuan Hong, Fan Dong, Steve Drew, Liangjie Xue, Jiayu Zhou

The recent decade witnessed a surge of increase in financial crimes across the public and private sectors, with an average cost of scams of $102m to financial institutions in 2022. Developing a mechanism for battling financial crimes is an impending task that requires in-depth collaboration from multiple institutions, and yet such collaboration imposed significant technical challenges due to the privacy and security requirements of distributed financial data. For example, consider the modern payment network systems, which can generate millions of transactions per day across a large number of global institutions. Training a detection model of fraudulent transactions requires not only secured transactions but also the private account activities of those involved in each transaction from corresponding bank systems. The distributed nature of both samples and features prevents most existing learning systems from being directly adopted to handle the data mining task. In this paper, we collectively address these challenges by proposing a hybrid federated learning system that offers secure and privacy-aware learning and inference for financial crime detection. We conduct extensive empirical studies to evaluate the proposed framework's detection performance and privacy-protection capability, evaluating its robustness against common malicious attacks of collaborative learning. We release our source code at https://github.com/illidanlab/HyFL .

📄 PDF Abstract BibTeX arXiv:2302.03654

Code (1)

illidanlab/hyfl 공식 구현 pytorch

Tasks

Federated LearningPrivacy Preserving

A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection

2025-07-15 · Abhishek Sawaika, Swetang Krishna, Tushar Tomar, Durga Pritam Suggisetti 외 arxiv

Rapid growth of digital transactions has led to a surge in fraudulent activities, challenging traditional detection methods in the financial sector. To tackle this problem, we introduce a specialised federated learning f…

Federated Learning Fraud Detection

HybridFL: A Federated Learning Approach for Financial Crime Detection

2026-02-22 · Afsana Khan, Marijn ten Thij, Guangzhi Tang, Anna Wilbik arxiv

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…

Federated Learning

HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning

2021-05-21 · Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Julian Matschinske, Jan Baumbach 외

Federated learning (FL) enables multiple clients to jointly train a global model under the coordination of a central server. Although FL is a privacy-aware paradigm, where raw data sharing is not required, recent studies…

BIG-bench Machine Learning Federated Learning Privacy Preserving

Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes

2026-02-18 · Srikumar Nayak, James Walmesley arxiv

Cross-border insider threats pose a critical challenge to government financial schemes, particularly when dealing with distributed, privacy-sensitive data across multiple jurisdictions. Existing approaches face fundament…

Federated Learning Causal Inference Graph Learning

Privacy-Preserving Federated Fraud Detection in Payment Transactions with NVIDIA FLARE

2026-03-13 · Holger R. Roth, Sarthak Tickoo, Mayank Kumar, Isaac Yang 외 arxiv

Fraud-related financial losses continue to rise, while regulatory, privacy, and data-sovereignty constraints increasingly limit the feasibility of centralized fraud detection systems. Federated Learning (FL) has emerged …

Federated Learning Anomaly Detection Fraud Detection