# Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection

arXiv: https://arxiv.org/abs/2401.10765 · HTML: https://mlsanity.com/paper/2401.10765/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 98 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 29 of 29)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [Privacy Preserving Vertical Federated Learning for Tree-based Models](https://mlsanity.com/paper/2008.06170/) (2020)
- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [SplitNN-driven Vertical Partitioning](https://mlsanity.com/paper/2008.04137/) (2020)
- [PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN](https://mlsanity.com/paper/2104.00489/) (2021)
- [Towards Federated Graph Learning for Collaborative Financial Crimes Detection](https://mlsanity.com/paper/1909.12946/) (2019)
- [FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data](https://mlsanity.com/paper/2103.03918/) (2021)
- [FederBoost: Private Federated Learning for GBDT](https://mlsanity.com/paper/2011.02796/) (2020)
- [Local Privacy, Data Processing Inequalities, and Statistical Minimax Rates](https://mlsanity.com/paper/1302.3203/) (2013)
- [Asymmetrical Vertical Federated Learning](https://mlsanity.com/paper/2004.07427/) (2020)
- [Large-Scale Secure XGB for Vertical Federated Learning](https://mlsanity.com/paper/2005.08479/) (2020)
- [Vertical Federated Learning without Revealing Intersection Membership](https://mlsanity.com/paper/2106.05508/) (2021)
- [Anomaly Detection in Blockchain Networks: A Comprehensive Survey](https://mlsanity.com/paper/2112.06089/) (2021)
- [Private Data Valuation and Fair Payment in Data Marketplaces](https://mlsanity.com/paper/2210.08723/) (2022)
- [Privacy Games: Optimal User-Centric Data Obfuscation](https://mlsanity.com/paper/1402.3426/) (2014)
- [Efficient Vertical Federated Learning with Secure Aggregation](https://mlsanity.com/paper/2305.11236/) (2023)
- [Earn While You Reveal: Private Set Intersection that Rewards Participants](https://mlsanity.com/paper/2301.03889/) (2023)
- [FadMan: Federated Anomaly Detection across Multiple Attributed Networks](https://mlsanity.com/paper/2205.14196/) (2022)
- [A Forward-secure Efficient Two-factor Authentication Protocol](https://mlsanity.com/paper/2208.02877/) (2022)
- [Privacy-Preserving Financial Anomaly Detection via Federated Learning & Multi-Party Computation](https://mlsanity.com/paper/2310.04546/) (2023)
- [Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection](https://mlsanity.com/paper/2310.19304/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications](https://mlsanity.com/paper/2403.01387/) (2024)
- [Towards Personalized Federated Learning via Comprehensive Knowledge Distillation](https://mlsanity.com/paper/2411.03569/) (2024)
- [Collaborative Synthetic Data for Privacy-Preserving Financial Fraud Detection Across Organizational Silos](https://mlsanity.com/paper/2609.33754/) (2026)

## Abstract (arXiv)

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(n2) 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.