Incentive mechanism of foundation model enabled cross-silo federated learning

The integration of foundation models (FMs) into cross-silo federated learning (FL) introduces transformative capabilities but also exacerbates strategic client behaviors, such as knowledge hoarding and free-riding, which degrade global model performance and system sustainability. Existing incentive mechanisms fail to address the knowledge hoarding and free-riding in FM-enabled FL. This paper proposes a novel incentive framework to harmonize client-server interests while suppressing adversarial behaviors. First, we propose a dynamic participant screening mechanism including pre-screening mechanism and confidence attenuation monitoring to filter low-quality updates and penalize intermittent participation. Second, we propose a cost-benefit balanced contribution metric to quantify clients' impacts by jointly evaluating accuracy gains, cost, and participation patterns. Third, we model the incentive mechanism as a two-stage Stackelberg game to establish symbiotic incentives, where the server adaptively adjust pricing strategy while clients optimize participation strategies. Simulation results demonstrate that our method can achieve up to 21.9% higher model accuracy and effectively filter malicious clients compared to existing benchmarks.