Latency Analysis of UAV-Assisted Vehicular Communications Using Personalized Federated Learning with Attention Mechanism - Abhishek Gupta; Xavier Fernando (Drones)
In this paper, unmanned aerial vehicle (UAV)-assisted vehicular communications are investigated to minimize latency and maximize the utilization of available UAV battery power. As communication and cooperation among UAV and vehicles is frequently required, a viable approach is to reduce the transmission of redundant messages. However, when the sensor data captured by the varying number of vehicles is not independent and identically distributed (non-i.i.d.), this becomes challenging. Hence, in order to group the vehicles with similar data distributions in a cluster, we utilize federated learning (FL) based on an attention mechanism. We jointly maximize the UAV’s available battery power in each transmission window and minimize communication latency. The simulation experiments reveal that the proposed personalized FL approach achieves performance improvement compared with baseline FL approaches. Our model, trained on the V2X-Sim dataset, outperforms existing methods on key performance indicators. The proposed FL approach with an attention mechanism offers a reduction in communication latency by up to 35% and a significant reduction in computational complexity without degradation in performance. Specifically, we achieve an improvement of approximately 40% in UAV energy efficiency, 20% reduction in the communication overhead, and 15% minimization in sojourn time.