Federated Learning for Real Time Fraud Detection in Decentralized Exchanges

Abstract

The rapid expansion of decentralized exchanges (DEXs) has amplified the need for real-time fraud detection mechanisms that ensure transparency and trust without compromising user privacy. Conventional centralized models for fraud analysis are increasingly ineffective in decentralized environments, where sensitive transaction data is distributed across multiple nodes. This paper proposes a federated learning-based framework for real-time fraud detection within DEX ecosystems, integrating privacy-preserving computation with adaptive intelligence. Drawing upon recent advancements in federated learning for credit card fraud detection [1], blockchain-enabled privacy protection [2], and communication-efficient anomaly detection in industrial IoT networks [3], the study develops a hybrid model that combines edge-level feature extraction with secure parameter aggregation. By leveraging blockchain-based consensus [4], [8] and decentralized model updates [5], the proposed architecture mitigates risks of single-point failure and data leakage. The framework is benchmarked against traditional centralized and semi-supervised approaches to evaluate performance under varying network latency and data heterogeneity conditions. Experimental simulations demonstrate significant improvements in detection accuracy, response latency, and model robustness. Furthermore, the study explores cross-domain applications in decentralized finance (DeFi), vehicular IoT, and cross-border payments [7], [9], highlighting federated learning’s potential as a cornerstone for future cyber-resilient financial systems. Ultimately, this research contributes to the growing discourse on secure, scalable, and transparent AI governance within decentralized trading infrastructures, advancing the intersection of FinTech, AI, and blockchain-driven risk management.