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%.

翻译：尽管联邦学习通过模型聚合提升了端到端自动驾驶的泛化能力，但传统单跳联邦学习因车辆与云端服务器间的远距离通信导致收敛速度缓慢。分层联邦学习通过引入中间边缘服务器克服了这一缺陷，然而有限通信资源与分层联邦学习性能之间的协同优化成为亟待解决的问题。本文提出一种基于优化的通信资源受限分层联邦学习框架，通过混合数据与模型聚合实现自动驾驶模型泛化误差的最小化。在CARLA仿真平台上的评估结果表明，所提出的CRCHFL框架既能加速收敛速度，又能增强联邦学习自动驾驶模型的泛化能力。在相同通信资源预算下，该框架较分层联邦学习性能提升10.33%，较单跳联邦学习性能提升12.44%。