Federated Incentive Learning: A Privacy-Preserving Framework for Ad Monetization and Creator Rewards in High-Concurrency Environments

Growing regulatory pressures have upended the historical pattern of cross-site behavioral targeting and measurement, forcing ad monetization systems to reconcile personalization, creator incentives, and privacy by design. This paper introduces Federated Incentive Learning (FIL), a privacy-preserving framework that optimizes ad placement and creator rewards in high-concurrency environments without transferring raw user data. FIL combines federated learning with on-device differential privacy, integrates Google’s Privacy Sandbox primitives for interest signals, on-device auctions, and privacy-preserving attribution, and imposes explicit fairness constraints for minimum exposure and region sensitive economic weighting. A global short video case study motivates design requirements such as sub-200 ms end-to-end decision latency and transparent incentive allocation. Methodologically, the study specifies a federated objective combining click-through and conversion prediction with incentive feedback, contrasts FedAvg and FedProx for heterogeneous clients, and calibrates Gaussian mechanisms to enforce an ε-differential privacy budget. Results indicate that FIL attains approximately 92 percent of the accuracy of centralized models while yielding a 25 percent improvement in small-to-medium advertiser return on investment and a 15 percent increase in creator earnings under weighted incentives and exposure guarantee. The framework demonstrates operational feasibility for privacy-sensitive, real-time monetization markets and contributes a governance-aware and equitable approach to ad delivery and creator compensation.