Internet of Things (IoTs) have been widely applied in Collaborative Intelligent Transportation Systems (C-ITS) for the prevention of road accidents. As one of the primary causes of road accidents in C-ITS, the efficient detection and early alarm of road hazards are of paramount importance. Given the importance, extensive research has explored this topic and obtained favorable results. However, most existing solutions only explore single-modality data, struggle with high computation and communication overhead, or suffer from the curse of high dimensionality in their privacy-preserving methodologies. To overcome these obstacles, in this paper, we introduce RoadFed, an innovative and private multimodal Federated learning-based system tailored for intelligent Road hazard detection and alarm. This framework encompasses an innovative Multimodal Road Hazard Detector, a communication-efficient federated learning approach, and a customized low-error-rate local differential privacy method crafted for high dimensional multimodal data. Experimental results reveal that the proposed RoadFed surpasses most existing systems in the self-gathered real-world and CrisisMMD public datasets. In particular, RoadFed achieves an accuracy of 96.42% with a mere 0.0351 seconds of latency and its communication cost is up to 1,000 times lower than existing systems in this field. It facilitates collaborative training with non-iid high dimensional multimodal real-world data across various data modalities on multiple edges while ensuring privacy preservation for road users.

翻译：物联网（IoTs）已广泛应用于协同智能交通系统（C-ITS）中以预防道路事故。作为C-ITS中道路事故的主要诱因之一，道路危险的高效检测与早期预警至关重要。鉴于其重要性，大量研究已对此展开探索并取得了良好成果。然而，现有方案大多仅利用单模态数据，受限于高计算与通信开销，或在其隐私保护方法中面临高维数据带来的挑战。为克服这些障碍，本文提出RoadFed——一种创新的、隐私保护的多模态联邦学习系统，专为智能道路危险检测与预警而设计。该框架包含创新的多模态道路危险检测器、通信高效的联邦学习方法，以及为高维多模态数据定制的低错误率本地差分隐私方法。实验结果表明，所提出的RoadFed在自采集真实世界数据集及CrisisMMD公共数据集上均优于现有大多数系统。具体而言，RoadFed实现了96.42%的准确率，延迟仅为0.0351秒，其通信成本比该领域现有系统降低高达1000倍。该系统能在保障道路用户隐私的前提下，支持跨多个边缘节点、针对不同数据模态的非独立同分布高维多模态真实世界数据进行协同训练。