A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy

To mitigate privacy leakage and performance issues in personalized advertising, this paper proposes a framework that integrates federated learning and differential privacy. The system combines distributed feature extraction, dynamic privacy budget allocati on, and robust model aggregation to balance model accuracy, communication overhead, and privacy protection. Multi - party secure computing and anomaly detection mechanisms further enhance system resilience against malicious attacks. Experimental results demo nstrate that the framework achieves dual optimization of recommendation accuracy and system efficiency while ensuring privacy, providing both a practical solution and a theoretical foundation for applying privacy protection technologies in advertisement re commendation. CCS CONCEPTS Computing methodologies ~ Artificial intelligence ~ Distributed artificial intelligence ~ Multi - agent systems Keywords F ederated learning; D ifferential privacy; A dvertisement recommendation; M odel aggregation optimization 1 INTRODUCTION Recent interest in privacy - preserving recommendation has led to widespread use of federated learning (FL) and differential privacy (DP).