Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detection
Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detection
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.
Aparna Gupta、Md. Saikat Islam Khan、Oshani Seneviratne、Stacy Patterson
财政、金融
Aparna Gupta,Md. Saikat Islam Khan,Oshani Seneviratne,Stacy Patterson.Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detection[EB/OL].(2024-08-02)[2025-08-18].https://arxiv.org/abs/2408.01609.点此复制
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