FedPrivRec: A Privacy-Preserving Federated Learning Framework for Real-Time E-Commerce Recommendation Systems

Abstract

This paper presents FedPrivRec, a novel privacy-preserving federated learning framework for real-time e-commerce recommendation systems that addresses the critical challenge of balancing personalization quality with user privacy protection. The proposed architecture implements a hierarchical federated approach comprising client devices, edge aggregators, and a central coordinator, enabling collaborative model training while keeping sensitive user data localized. FedPrivRec incorporates differential privacy mechanisms with adaptive noise calibration to provide formal privacy guarantees against reconstruction and inference attacks. The framework features a secure aggregation protocol ensuring individual contributions remain indiscernible while preserving statistical utility of aggregated updates. Adaptive real-time learning strategies dynamically adjust model complexity, update frequency, and privacy parameters based on contextual factors, while distributed caching significantly reduces inference latency without compromising privacy guarantees. Comprehensive evaluation across multiple real-world e-commerce datasets demonstrates that FedPrivRec achieves 95.7% of the recommendation accuracy of centralized approaches at privacy budget ε=1.0, outperforming existing privacy-preserving methods by 14.3%. The framework reduces communication requirements by 57% compared to traditional federated recommendation systems while maintaining real-time performance under varied load conditions. FedPrivRec establishes a new state-of-the-art in privacy-utility balance for recommendation systems, enabling regulatory compliance without sacrificing personalization quality.