FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles
This addresses data heterogeneity issues in federated learning for autonomous vehicle traffic object classification, offering an incremental improvement over existing methods.
The paper tackles the challenge of non-IID data in federated learning for autonomous vehicles by proposing FedRAV, a hierarchical framework that divides areas into sub-regions for personalized and regional models, improving accuracy by at least 3.69% on real-world datasets.
The emerging federated learning enables distributed autonomous vehicles to train equipped deep learning models collaboratively without exposing their raw data, providing great potential for utilizing explosively growing autonomous driving data. However, considering the complicated traffic environments and driving scenarios, deploying federated learning for autonomous vehicles is inevitably challenged by non-independent and identically distributed (Non-IID) data of vehicles, which may lead to failed convergence and low training accuracy. In this paper, we propose a novel hierarchically Federated Region-learning framework of Autonomous Vehicles (FedRAV), a two-stage framework, which adaptively divides a large area containing vehicles into sub-regions based on the defined region-wise distance, and achieves personalized vehicular models and regional models. This approach ensures that the personalized vehicular model adopts the beneficial models while discarding the unprofitable ones. We validate our FedRAV framework against existing federated learning algorithms on three real-world autonomous driving datasets in various heterogeneous settings. The experiment results demonstrate that our framework outperforms those known algorithms, and improves the accuracy by at least 3.69%. The source code of FedRAV is available at: https://github.com/yjzhai-cs/FedRAV.