Federated Graph Neural Networks for Dynamic Computation Offloading in Vehicular Networks

With the increasing number of Internet of Things devices and sensors in vehicular network, a huge amount of data is generated. Vehicle Edge Computing (VEC) utilises the computation resources at the edge of the network and can efficiently process this big data through computational offloading techniques. However, due to the neglect of communication network relationships among vehicles, current Vehicle-to-Vehicle (V2V) computation offloading schemes encounter challenges such as high communication latency, substantial communication overhead, and the wastage of computation resources. To address these challenges, we design a computation offloading mechanism based on Federated Graph Neural Network (GNN) for vehicular networks, that is, vehicular FedGNN (V-FedGNN). Firstly, our modeling approach considers features including vehicle speed, location, available resources, and wireless network, which are embedded in the graph structure. Secondly, we design weighted vehicular communication network topology and propose weighted total delay optimization problem. Finally, this paper proposes a prediction model based on Federated Learning (FL) and GNN to minimize the weighted computation offloading delays among vehicle nodes. Experimental results demonstrate that our proposed scheme achieves high offloading prediction accuracy, with an average value of 98.1% and achieves low offloading latency, correspondingly.