Abstract
Federated Learning (FL) enables collaborative training of autonomous driving (AD) models across distributed vehicles while preserving data privacy. However, FL encounters critical challenges such as poor generalization and slow convergence due to non-independent and identically distributed (non-IID) data from diverse driving environments. To overcome these obstacles, we introduce Federated Deep Supervision and Regularization (FedDSR), a paradigm that incorporates multi-access intermediate layer supervision and regularization within federated AD system. Specifically, FedDSR comprises following integral strategies: (I) to select multiple intermediate layers based on predefined architecture-agnostic standards. (II) to compute mutual information (MI) and negative entropy (NE) on those selected layers to serve as intermediate loss and regularizer. These terms are integrated into the output-layer loss to form a unified optimization objective, enabling comprehensive optimization across the network hierarchy. (III) to aggregate models from vehicles trained based on aforementioned rules of (I) and (II) to generate the global model on central server. By guiding and penalizing the learning of feature representations at intermediate stages, FedDSR enhances the model generalization and accelerates model convergence for federated AD. We then take the semantic segmentation task as an example to assess FedDSR and apply FedDSR to multiple model architectures and FL algorithms. Extensive experiments demonstrate that FedDSR achieves up to 8.93% improvement in mIoU and 28.57% reduction in training rounds, compared to other FL baselines, making it highly suitable for practical deployment in federated AD ecosystems.
Abstract (translated)
联邦学习（FL）使分散车辆之间能够协作训练自动驾驶（AD）模型，同时保护数据隐私。然而，由于来自不同驾驶环境的数据不具备独立同分布特性（non-IID），联邦学习面临着诸如泛化能力差和收敛速度慢等关键挑战。为克服这些障碍，我们引入了联邦深度监督与正则化（FedDSR）范式，在该范式中，多通道中间层监督和正则化被整合到联邦自动驾驶系统中。具体而言，FedDSR包含以下核心策略：(I) 根据预定义的架构无关标准选择多个中间层。(II) 在所选的这些层上计算互信息（MI）和负熵（NE），以充当中间损失函数和正则化项。这些术语被整合到输出层损失中，形成统一的优化目标，从而在整个网络层次结构中实现全面优化。(III) 将基于上述(I)和(II)规则训练的车辆模型聚合在中央服务器上生成全局模型。 通过指导和惩罚中间阶段特征表示的学习过程，FedDSR提升了模型的泛化能力和加快了联邦自动驾驶中的模型收敛速度。然后以语义分割任务为例评估FedDSR，并将其应用于多种模型架构和联邦学习算法中。广泛的实验表明，与其它联邦学习基准相比，FedDSR在mIoU方面提高了高达8.93%，并减少了28.57%的训练轮次，这使得它非常适合部署于联邦自动驾驶生态系统中。
URL
https://arxiv.org/abs/2512.06676
