Federated Learning (FL) is a data-minimization approach enabling collaborative model training across diverse clients with local data, avoiding direct data exchange. However, state-of-the-art FL solutions to identify fraudulent financial transactions exhibit a subset of the following limitations. They (1) lack a formal security definition and proof, (2) assume prior freezing of suspicious customers' accounts by financial institutions (limiting the solutions' adoption), (3) scale poorly, involving either $O(n^2)$ computationally expensive modular exponentiation (where $n$ is the total number of financial institutions) or highly inefficient fully homomorphic encryption, (4) assume the parties have already completed the identity alignment phase, hence excluding it from the implementation, performance evaluation, and security analysis, and (5) struggle to resist clients' dropouts. This work introduces Starlit, a novel scalable privacy-preserving FL mechanism that overcomes these limitations. It has various applications, such as enhancing financial fraud detection, mitigating terrorism, and enhancing digital health. We implemented Starlit and conducted a thorough performance analysis using synthetic data from a key player in global financial transactions. The evaluation indicates Starlit's scalability, efficiency, and accuracy.

翻译：联邦学习（FL）是一种数据最小化方法，支持跨多个本地数据客户端进行协作模型训练，且无需直接交换数据。然而，现有用于识别欺诈性金融交易的先进FL解决方案存在以下部分局限性：（1）缺乏正式的安全定义与证明；（2）假设金融机构已事先冻结可疑客户账户（限制了方案的采纳）；（3）扩展性差，涉及$O(n^2)$计算开销大的模幂运算（其中$n$为金融机构总数）或效率极低的全同态加密；（4）假设各方已完成身份对齐阶段，因此将其排除在实现、性能评估及安全性分析之外；（5）难以抵御客户中途退出。本文提出Starlit——一种新颖的可扩展隐私保护FL机制，克服了上述局限性。该机制广泛应用于金融欺诈检测增强、反恐及数字健康等领域。我们实现了Starlit，并利用全球金融交易关键参与者的合成数据进行了全面性能分析。评估结果表明了Starlit的可扩展性、高效性与准确性。