Federated learning has been widely applied in autonomous driving since it enables training a learning model among vehicles without sharing users' data. However, data from autonomous vehicles usually suffer from the non-independent-and-identically-distributed (non-IID) problem, which may cause negative effects on the convergence of the learning process. In this paper, we propose a new contrastive divergence loss to address the non-IID problem in autonomous driving by reducing the impact of divergence factors from transmitted models during the local learning process of each silo. We also analyze the effects of contrastive divergence in various autonomous driving scenarios, under multiple network infrastructures, and with different centralized/distributed learning schemes. Our intensive experiments on three datasets demonstrate that our proposed contrastive divergence loss significantly improves the performance over current state-of-the-art approaches.

翻译：联邦学习已被广泛应用于自主驾驶领域，因为它能够在车辆间训练学习模型而无需共享用户数据。然而，自主驾驶车辆的数据通常存在非独立同分布问题，这可能对学习过程的收敛性产生负面影响。本文提出了一种新的对比散度损失，通过在本地学习过程中减少各数据站传输模型中散度因素的影响，来解决自主驾驶中的非独立同分布问题。我们还分析了对比散度在多种自主驾驶场景、多种网络基础设施以及不同集中式/分布式学习方案下的影响。在三个数据集上的大量实验表明，我们提出的对比散度损失显著优于当前最先进的方法。