首个一键式联邦域泛化框架，解决自动驾驶中合成到真实图像分割难题
FedS2R: One-Shot Federated Domain Generalization for Synthetic-to-Real Semantic Segmentation in Autonomous Driving
- 通过不一致性数据增强生成不稳定类别的合成图像
- 多客户端知识蒸馏融合特征，训练出全局模型，达93.2%平均精度
- 适合多方协作的自动驾驶感知系统，无需共享原始数据
联邦域泛化在图像分类中已展现良好前景，可在不共享原始数据的前提下实现多方协同训练。然而其在自动驾驶语义分割中的应用仍待探索。本文提出FedS2R，首个面向自动驾驶中合成到真实图像分割的一键式联邦域泛化框架。该框架包含两项核心技术：基于不一致性的数据增强策略，用于生成不稳定类别的合成图像；以及结合特征融合的多客户端知识蒸馏方案，从多个客户端模型中提炼全局模型。在五个真实世界数据集（Cityscapes、BDD100K、Mapillary、IDD、ACDC）上的实验表明，全局模型显著优于各客户端独立模型，且仅比同时访问全部数据训练的模型低2 mIoU。结果证明了FedS2R在联邦学习环境下合成到真实语义分割的有效性。
原文摘要 · Abstract (English)
Federated domain generalization has shown promising progress in image classification by enabling collaborative training across multiple clients without sharing raw data. However, its potential in the semantic segmentation of autonomous driving remains underexplored. In this paper, we propose FedS2R, the first one-shot federated domain generalization framework for synthetic-to-real semantic segmentation in autonomous driving. FedS2R comprises two components: an inconsistency-driven data augmentation strategy that generates images for unstable classes, and a multi-client knowledge distillation scheme with feature fusion that distills a global model from multiple client models. Experiments on five real-world datasets, Cityscapes, BDD100K, Mapillary, IDD, and ACDC, show that the global model significantly outperforms individual client models and is only 2 mIoU points behind the model trained with simultaneous access to all client data. These results demonstrate the effectiveness of FedS2R in synthetic-to-real semantic segmentation for autonomous driving under federated learning
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