In the pursuit of refining precise perception models for fully autonomous driving, continual online model training becomes essential. Federated Learning (FL) within vehicular networks offers an efficient mechanism for model training while preserving raw sensory data integrity. Yet, FL struggles with non-identically distributed data (e.g., quantity skew), leading to suboptimal convergence rates during model training. In previous work, we introduced FedLA, an innovative Label-Aware aggregation method addressing data heterogeneity in FL for generic scenarios. In this paper, we introduce FedProx+LA, a novel FL method building upon the state-of-the-art FedProx and FedLA to tackle data heterogeneity, which is specifically tailored for vehicular networks. We evaluate the efficacy of FedProx+LA in continuous online object detection model training. Through a comparative analysis against conventional and state-of-the-art methods, our findings reveal the superior convergence rate of FedProx+LA. Notably, if the label distribution is very heterogeneous, our FedProx+LA approach shows substantial improvements in detection performance compared to baseline methods, also outperforming our previous FedLA approach. Moreover, both FedLA and FedProx+LA increase convergence speed by 30% compared to baseline methods.

翻译：在完善全自动驾驶精准感知模型的探索中，持续在线模型训练至关重要。车联网中的联邦学习（FL）提供了一种高效模型训练机制，同时保留原始数据完整性。然而，FL在处理非独立同分布数据（如数量倾斜）时存在困难，导致模型训练收敛速度欠佳。在先前工作中，我们提出了FedLA——一种创新的标签感知聚合方法，用于解决通用场景中的数据异构性问题。本文进一步提出FedProx+LA，这是一种基于先进FedProx和FedLA方法的新型FL方法，专为车联网场景定制以应对数据异构性。我们评估了FedProx+LA在连续在线目标检测模型训练中的效能。通过与常规方法及先进方法的对比分析，结果表明FedProx+LA具有更优的收敛速度。值得注意的是，当标签分布高度异构时，我们的FedProx+LA方法在检测性能上较基线方法有显著提升，同时超越了先前提出的FedLA方法。此外，与基线方法相比，FedLA与FedProx+LA均将收敛速度提升了30%。