Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detection
<|reference_start|>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.<|reference_end|>
@article{khan2024fed-rd:,
    title={Fed-RD: Privacy-Preserving Federated Learning for Financial Crime
  Detection},
    author={Md. Saikat Islam Khan, Aparna Gupta, Oshani Seneviratne, Stacy
  Patterson},
    journal={arXiv preprint arXiv:2408.01609},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01609},
    primaryClass={cs.CE}
}