Unmanned aerial vehicle swarm-assisted reliable federated learning for traffic flow prediction

Abstract

Unmanned Aerial Vehicle (UAV) swarms, as efficient and flexible monitoring tools, can collect real-time traffic information over extensive areas. However, UAV swarms engaged in traffic monitoring are vulnerable to network attacks and privacy breaches, leading to data distortion and compromised system performance. To address these security challenges and incentivize UAV participation, we propose CI-AGFL, a federated learning (FL)-based swarm intelligence approach that enables distributed traffic flow prediction through seamless information sharing and fusion between ground vehicles and UAV swarms. In CI-AGFL, ground vehicles train local models, which are then aggregated into a global model by UAV swarms using a robust, decentralized aggregation method grounded in consensus confirmation. Furthermore, a fuzzy membership method is employed to evaluate UAV trustworthiness during the model aggregation phase. Additionally, we introduce a reputation-based multi-dimensional contract theory incentive mechanism to optimize UAV participation in federated learning tasks, dynamically balancing energy consumption with training latency to ensure accurate, real-time traffic flow predictions. Experimental results demonstrate that CI-AGFL outperforms three advanced traffic flow prediction methods, achieving improvements of 8.2% to 22.8% in MAE, MSE, RMSE, and MAPE metrics, while significantly enhancing model convergence.