Privacy-Preserving Personalized Recommendation with Federated Learning for Cross-Domain User Modeling

Abstract

Personalized recommender systems increasingly depend on cross-domain user modeling to overcome data sparsity, improve cold-start performance, and produce contextually relevant suggestions across heterogeneous digital services. However, centralized collection and processing of user interaction data expose individuals to privacy risks and create legal tensions with modern data protection regimes. Federated learning offers a promising alternative by enabling collaborative model training without transferring raw user data to a central server. This article presents a system-level examination of privacy-preserving personalized recommendation with federated learning for cross-domain user modeling. It analyzes architectural choices, privacy mechanisms, structural trade-offs, robustness, fairness, governance, and deployment sustainability. The discussion emphasizes that privacy-preserving cross-domain recommendation is not solely an algorithmic challenge; it requires careful coordination among client-side representation learning, edge aggregation, domain-specific adaptation, incentive design, and regulatory compliance. We argue that the design space is shaped by fundamental tensions between personalization depth and communication efficiency, local differential privacy and model utility, cross-domain transfer and negative interference, and participant autonomy and system-wide consistency. The article further considers how popularity and demographic biases can be amplified by federated optimization and how fairness constraints interact with resource heterogeneity. It concludes by outlining forward-looking research directions in hierarchical trust architectures, adaptive privacy budgeting, auditable cross-domain governance, and sustainable deployment models for privacy-preserving personalization infrastructures.