Federated Learning Optimizing Multi-Scenario Ad Targeting and Investment Returns in Digital Advertising

Abstract

This study investigates the use of Federated Learning (FL) in optimizing multi-scenario advertising targeting and improving return on investment (ROI) in digital advertising. With the rapid growth of digital advertising, traditional methods face significant challenges due to fragmented user data across multiple platforms and devices, as well as concerns over user privacy. FL enables cross-platform data collaboration by training machine learning models locally on user devices, ensuring that raw data is never shared, thus protecting user privacy. The proposed FL-based advertising optimization framework aims to enhance ad targeting precision while maintaining privacy. Experimental results on an e-commerce platform show that the FL framework increases the click-through rate by 25% and the conversion rate by 18%, demonstrating its potential to improve advertising effectiveness in complex, multi-scenario environments. This approach not only provides a privacy-preserving solution for advertisers but also offers a scalable model for integrating data from multiple platforms to optimize ad strategies and maximize ROI.