Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detection
Computational Engineering, Finance, and Science
2024-08-06 v1
Abstract
We introduce Federated Learning for Relational Data (Fed-RD), a novel privacy-preserving federated learning algorithm specifically developed for financial transaction datasets partitioned vertically and horizontally across parties. Fed-RD strategically employs differential privacy and secure multiparty computation to guarantee the privacy of training data. We provide theoretical analysis of the end-to-end privacy of the training algorithm and present experimental results on realistic synthetic datasets. Our results demonstrate that Fed-RD achieves high model accuracy with minimal degradation as privacy increases, while consistently surpassing benchmark results.
Keywords
Cite
@article{arxiv.2408.01609,
title = {Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detection},
author = {Md. Saikat Islam Khan and Aparna Gupta and Oshani Seneviratne and Stacy Patterson},
journal= {arXiv preprint arXiv:2408.01609},
year = {2024}
}