Evaluating Deep Learning and Machine Learning Models in Federated Learning for Credit Card Fraud Detection: A Comparative Study

Sener Ali
Additional contact information
Sener Ali: The Bucharest University of Economic Studies, Romania

Database Systems Journal, 2025, vol. 16, issue 1, 37-44

Abstract: This study evaluates machine learning and deep learning algorithms for credit card fraud detection within a federated learning framework. With digital banking’s rapid growth facilitating customer access yet exposing new fraud vectors, real-time detection is critical. The paper trains XGBoost and a neural network on a highly imbalanced public dataset, reflecting real fraud scenarios. Both models achieve high accuracy, but the neural network consistently outperforms XGBoost on precision, recall, and F1 score, demonstrating deep learning’s superior capability in privacy-preserving, collaborative fraud detection.

Keywords: Federated Learning; Credit Card Fraud Detection; Privacy Protection; Machine Learning; Deep Learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.dbjournal.ro/archive/36/36_4.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aes:dbjour:v:16:y:2025:i:1:p:37-44

Access Statistics for this article

Database Systems Journal is currently edited by Ion Lungu

More articles in Database Systems Journal from Academy of Economic Studies - Bucharest, Romania Contact information at EDIRC.
Bibliographic data for series maintained by Adela Bara ().