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.

翻译：我们提出了面向关系型数据的联邦学习（Fed-RD），这是一种新颖的隐私保护联邦学习算法，专为在参与方之间垂直和水平划分的金融交易数据集而设计。Fed-RD策略性地结合差分隐私和安全多方计算，以确保训练数据的隐私性。我们对训练算法的端到端隐私性进行了理论分析，并在真实的合成数据集上给出了实验结果。结果表明，Fed-RD在实现高模型精度的同时，随着隐私保护强度的增加，性能下降极小，并且始终优于基准结果。