This research introduces a novel federated learning framework that enables cross-domain click-through rate prediction while maintaining strong privacy protections.

• Combines Large Language Models with federated learning to bridge domain gaps without sharing raw user data
• Implements Adaptive Local Differential Privacy to balance privacy protection with prediction accuracy
• Achieves up to 15% improvement in CTR prediction performance across heterogeneous domains
• Demonstrates practical solutions for organizations facing data silos and privacy regulations

This breakthrough addresses critical security challenges in recommendation systems by eliminating the need for centralized data sharing while still enabling effective cross-domain knowledge transfer.

Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation