mlsanityData to 7 Oct 2026

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

arXiv 2401.10765 ↗First posted 19 Jan 2024cs.LG cs.CRcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 29 of its 98 references that are in the map. First posted on arXiv 19 Jan 2024.

Builds on

29 of 98 references.

References · 29

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2020Adaptive Federated Optimizationcited by 1,036
  5. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  6. 2019Federated Machine Learning: Concept and Applicationscited by 409
  7. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  8. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  9. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  10. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  11. 2021AI in Finance: Challenges, Techniques and Opportunitiescited by 81
  12. 2020SplitNN-driven Vertical Partitioningcited by 42
17 more
  1. 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
  2. 2019Towards Federated Graph Learning for Collaborative Financial Crimes Detectioncited by 33
  3. 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
  4. 2020FederBoost: Private Federated Learning for GBDTcited by 27
  5. 2013Local Privacy, Data Processing Inequalities, and Statistical Minimax Ratescited by 26
  6. 2020Asymmetrical Vertical Federated Learningcited by 26
  7. 2020Large-Scale Secure XGB for Vertical Federated Learningcited by 26
  8. 2021Vertical Federated Learning without Revealing Intersection Membershipcited by 19
  9. 2021Anomaly Detection in Blockchain Networks: A Comprehensive Surveycited by 16
  10. 2022Private Data Valuation and Fair Payment in Data Marketplacescited by 12
  11. 2014Privacy Games: Optimal User-Centric Data Obfuscationcited by 7
  12. 2023Efficient Vertical Federated Learning with Secure Aggregationcited by 3
  13. 2023Earn While You Reveal: Private Set Intersection that Rewards Participantscited by 2
  14. 2022FadMan: Federated Anomaly Detection across Multiple Attributed Networkscited by 1
  15. 2022A Forward-secure Efficient Two-factor Authentication Protocolcited by 1
  16. 2023Privacy-Preserving Financial Anomaly Detection via Federated Learning & Multi-Party Computationcited by 1
  17. 2023Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detectioncited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 1.

3 papers.

  1. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  2. 2024Towards Personalized Federated Learning via Comprehensive Knowledge Distillationcited by 1
  3. 2026Collaborative Synthetic Data for Privacy-Preserving Financial Fraud Detection Across Organizational Siloscited by 0