Overcoming Resource Bottlenecks in Vehicular Federated Learning: A Cluster-Based and QoS-Aware Approach

Federated learning (FL) is a promising approach for processing on-board data in vehicular networks due to its distributed nature and its ability to accurately and efficiently handle the large amount of sensed data. However, training and transmitting the model parameters during FL process can consume a significant amount of energy and time, which is not suitable for applications with strict real-time requirements. Moreover, the dynamicity of the vehicular network, as well as the varying capabilities of each vehicle, can impact the performance of the training process, bringing to the forefront the optimization of the participants selection and their resources. In this paper, we propose VOC-FL, a Vehicular-based Offloading and Clustering framework supported by FL. The proposed scheme bypasses communication bottlenecks by enabling groups of vehicles to train models simultaneously, with only the Cluster Head (CH) sending the aggregated results of each cluster to the roadside units for further processing. To form the clusters, we select a CH for each cluster based on multiple metrics, including stability, computational resources, bandwidth, and network topology. Moreover, the CH runs an offloading strategy that allows struggling nodes with limited computational resources to offload their tasks to other nodes with enough resources within the cluster, enabling efficient and effective use of resources.