Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving
This addresses communication efficiency for federated learning in autonomous driving, offering an incremental improvement over existing hierarchical methods.
The paper tackles slow convergence in federated learning for autonomous driving by proposing a communication resource-constrained hierarchical framework, which accelerates convergence and improves generalization, outperforming baseline methods by 10.33% and 12.44% under the same resource budget.
While federated learning (FL) improves the generalization of end-to-end autonomous driving by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence rate due to long-range communications among vehicles and cloud server. Hierarchical federated learning (HFL) overcomes such drawbacks via introduction of mid-point edge servers. However, the orchestration between constrained communication resources and HFL performance becomes an urgent problem. This paper proposes an optimization-based Communication Resource Constrained Hierarchical Federated Learning (CRCHFL) framework to minimize the generalization error of the autonomous driving model using hybrid data and model aggregation. The effectiveness of the proposed CRCHFL is evaluated in the Car Learning to Act (CARLA) simulation platform. Results show that the proposed CRCHFL both accelerates the convergence rate and enhances the generalization of federated learning autonomous driving model. Moreover, under the same communication resource budget, it outperforms the HFL by 10.33% and the SFL by 12.44%.