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 further improves the performance over current state-of-the-art approaches.

翻译：联邦学习因其能够在车辆间训练学习模型而无需共享用户数据，已在自动驾驶领域得到广泛应用。然而，自动驾驶车辆的数据常面临非独立同分布（non-IID）问题，可能对学习过程的收敛性产生负面影响。本文提出一种新的对比散度损失函数，通过降低本地学习过程中各数据孤岛所传输模型中的散度因子影响，来解决自动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动驾驶场景、不同网络基础设施及集中式/分布式学习方案下的效果。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函数进一步提升了当前最优方法的性能。