A Comprehensive Investigation of Fraud Detection Behavior in Federated Learning

Abstract

This research delves into the application of Federated Learning (FL) models for detecting fraud across different financial bodies. FL facilitates decentralized training of models using local data, ensuring privacy, crucial for handling sensitive financial data. The comparison involves three machine learning models - Artificial Neural Networks (ANN), Random Forest (RF), and Convolutional Neural Networks (CNN) - to assess their efficacy in the FL context. While ANN and CNN demonstrate strong capacity in identifying complex fraud patterns, their communication efficiency and overfitting challenges are significant. In contrast, RF offers more robustness to Non-independent and Identically Distributed (non-IID) data and is less prone to overfitting, though it poses communication overhead issues. This paper also highlights the challenges of FL in fraud detection, including data heterogeneity, communication costs, and security risks. This paper proposed future research directions, emphasizing model personalization, communication optimization, and advanced privacy-preserving techniques. By addressing these challenges, FL can offer scalable, secure solutions for real-time fraud detection, ensuring the protection of sensitive financial data while enhancing detection accuracy across diverse data sources.