PRIVACY-PRESERVING CRYPTOCURRENCY FRAUD DETECTION USING FEDERATED LEARNING

Abstract

Decentralized finance (DeFi) platforms have experienced a rapid increase in fraudulent activities such as price manipulation, wash trading, and anomalous transaction behavior, while traditional centralized fraud detection methods remain unsuitable due to privacy and regulatory constraints. This study proposes a privacy-preserving fraud detection framework using federated learning, enabling multiple decentralized entities to collaboratively train a machine learning model without sharing raw transaction data. A real-world decentralized exchange (DEX) dataset containing over 100,000 transactions is preprocessed and enhanced through feature engineering techniques capturing swap rate deviations, transaction volume anomalies, and temporal patterns. In the absence of labeled fraud data, a heuristic-based labeling approach is employed to simulate realistic fraud scenarios. A Logistic Regression model is trained across multiple distributed client nodes, with model parameters aggregated using the Federated Averaging (FedAvg) algorithm over several communication rounds. The experimental findings show that the federated model delivers results similar to centralized methods while preserving data privacy, proving it to be an efficient solution for secure and scalable fraud detection in decentralized financial environments.