Reinforcement Learning-Based Incentive Scheme for Federated Learning in Vehicular Networks

Federated learning (FL) in vehicular networks faces significant challenges due to client nodes’ limited resources and highly dynamic network conditions, which hider active participation and degrade model performance. This paper proposes a novel incentive mechanism that explicitly addresses these challenges by combining a fuzzy logic-based contribution estimation algorithm with a reinforcement learning-based rewarding module. The estimation algorithm evaluates each client’s potential contribution by considering mobility patterns, link quality, and computational capacity, while the rewarding module dynamically adjusts incentives based on both estimated potential and actual performance. The estimation algorithm evaluates each client’s potential contribution by considering mobility patterns, link quality, and computational capacity, while the rewarding module dynamically adjusts incentives based on both estimated potential and actual performance. Extensive simulations on realistic vehicular network scenarios show that our scheme increases client participation by approximately 37% and shortens the average convergence time by about 30 global epoches compared with traditional incentive approaches. These results demonstrate that our method effectively enhances cooperation and model performance in resource-constrained, dynamic vehicular FL environments.