Multi-Stage Federated Learning with non-IID Data in Internet of Vehicles

The Internet of vehicles (IoV) plays an indispensable role in the construction of smart cities, where cars are not just a means of transportation but also a crucial medium for information collection and transmission in the era of big data. With the rapid growth in the volume of data collected from vehicles and the increased awareness of privacy protection, ensuring users’ data security and preventing data breaches in IoV has become an urgent issue to address. Federated learning, as a ‘data-does-not-move, model-moves’ approach, offers a feasible method for protecting user privacy while achieving excellent performance. However, because of the differences of devices, regions and individual habits, data collected from multiple vehicles typically exhibits non-independent and identically distributed (non-IID) characteristics. Traditional federated learning algorithms have slow model convergence when processing non-IID data. In response to this challenge, this paper proposes a multi-stage federated learning algorithm with non-IID data in IoV, named FedWO. In Stage 1, FedWO utilizes the federated averaging algorithm to expedite the global model in reaching a basic level of accuracy. In Stage 2, it employs weighted federated learning, where the weight of a vehicle in the global model is calculated based on its data characteristics. This aggregation results in an improved global model. Moreover, we design a transmission control strategy to reduce communication overhead caused by model transmission. The Stage 3 involves personalized computation, where each vehicle employs its own data for personalized learning, fine-tuning the local model to obtain a model more aligned with local data. We conducted experimental evaluations using a driving behavior dataset. The results demonstrate that, compared with traditional methods, FedWO preserves data privacy while improving the accuracy of algorithms in non-IID data scenarios.