  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.
