# Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detection

arXiv: https://arxiv.org/abs/2408.01609 · HTML: https://mlsanity.com/paper/2408.01609/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2024.

## Built on by (most cited first, 4 of 4)

- [FedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphs](https://mlsanity.com/paper/2608.24846/) (2026)
- [HybridFL: A Federated Learning Approach for Financial Crime Detection](https://mlsanity.com/paper/2602.19207/) (2026)
- [FedV-KGQA in Practice: Design Lessons and an Interactive Prototype](https://mlsanity.com/paper/2609.13661/) (2026)
- [Cost Characterization of Vertically Partitioned Federated Knowledge Graphs](https://mlsanity.com/paper/2609.13664/) (2026)

## Abstract (arXiv)

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.