Delay-Sensitive Federated Learning in Vehicular Networks via Multi-Dimensional Awareness Clustering and Computation Offloading

In vehicular networks, various intelligent tasks, such as traffic flow prediction, object detection, and route planning, rely on vehicle collaboration for successful completion. Federated learning (FL), as a privacy-preserving collaborative distributed deep learning paradigm, has been increasing applications in vehicular networks. However, due to limited communication resources, frequent parameter transmissions between vehicles and remote base stations during the FL process result in the significant delays. Additionally, FL faces performance degradation challenges due to non-independent and identically distributed (non-IID) data. Moreover, the straggler effect in FL further prolongs the overall delay of FL. To address these issues, we propose a novel trust-based computation offloading assisted clustered federated learning framework for vehicular networks. In this framework, vehicles are grouped into different clusters and the straggling vehicles can offload partial computation tasks to their corresponding cluster heads. Specifically, we first analyze the impact of clustering strategy on learning loss and then design a multi-dimensional awareness clustering scheme that jointly considers vehicles’ physical domain, trust degree, and data distribution. Moreover, we formulate a total delay minimization problem for FL by optimizing computation offloading and bandwidth allocation decisions for each cluster. To solve this, we propose a dual actor-critic cross deep deterministic policy gradient (DC-DDPG) algorithm which determines the optimal offloading ratio and bandwidth allocation for each cluster. Extensive experiments demonstrate that our proposed scheme significantly outperforms baseline methods in reducing FL latency.