Privacy-Aware AI Advertising Systems: A Federated Learning Framework for Cross-Platform Personalization

Digital advertising ecosystems increasingly rely on large-scale artificial intelligence infrastructures that personalize marketing messages, optimize bidding strategies, and allocate attention across millions of users and advertisers. Traditional advertising architectures depend heavily on centralized data aggregation, where behavioral logs from multiple platforms are combined to train large predictive models. While this approach enables highly accurate personalization, it also raises significant concerns related to privacy protection, regulatory compliance, data governance, and systemic concentration of informational power. As privacy regulations expand globally and user expectations regarding data protection intensify, the advertising industry faces increasing pressure to develop new system architectures capable of preserving personalization capabilities while minimizing direct data collection and centralized storage. This paper proposes a privacy-aware advertising framework based on federated learning for cross-platform personalization. Rather than treating federated learning solely as a distributed optimization technique, the framework conceptualizes it as a socio-technical infrastructure that redistributes data custody, computational responsibilities, and governance accountability across multiple actors in the advertising ecosystem. The study examines how decentralized model training can enable collaborative personalization across advertisers, publishers, and devices without requiring raw behavioral data to leave local environments. Particular attention is given to system-level design challenges including heterogeneous data distributions, delayed feedback signals, adversarial manipulation risks, fairness constraints, and cross-jurisdictional regulatory compliance. The paper develops a multi-layer architectural model integrating local representation learning, secure aggregation protocols, differential privacy mechanisms, and policy-aware governance structures. It further explores the implications of federated advertising systems for market competition, algorithmic fairness, and institutional accountability. The analysis demonstrates that federated learning can significantly reduce centralized data risks while maintaining effective personalization performance when combined with robust coordination protocols and transparent governance frameworks. The paper concludes that privacy-aware federated infrastructures represent a promising direction for the future evolution of digital advertising ecosystems.