Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detection
Md. Saikat Islam Khan, Aparna Gupta, Oshani Seneviratne, Stacy Patterson · 2024
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 - Rdachieves high model accuracy with minimal degradation as privacy increases, while consistently surpassing benchmark results.