With the proliferation of GPS-equipped edge devices, huge trajectory data is generated and accumulated in various domains, motivating a variety of urban applications. Due to the limited acquisition capabilities of edge devices, a lot of trajectories are recorded at a low sampling rate, which may lead to the effectiveness drop of urban applications. We aim to recover a high-sampled trajectory based on the low-sampled trajectory in free space, i.e., without road network information, to enhance the usability of trajectory data and support urban applications more effectively. Recent proposals targeting trajectory recovery often assume that trajectories are available at a central location, which fail to handle the decentralized trajectories and hurt privacy. To bridge the gap between decentralized training and trajectory recovery, we propose a lightweight framework, LightTR, for federated trajectory recovery based on a client-server architecture, while keeping the data decentralized and private in each client/platform center (e.g., each data center of a company). Specifically, considering the limited processing capabilities of edge devices, LightTR encompasses a light local trajectory embedding module that offers improved computational efficiency without compromising its feature extraction capabilities. LightTR also features a meta-knowledge enhanced local-global training scheme to reduce communication costs between the server and clients and thus further offer efficiency improvement. Extensive experiments demonstrate the effectiveness and efficiency of the proposed framework.

翻译：随着配备GPS的边缘设备的广泛应用，各领域产生并积累了海量轨迹数据，推动了多种城市应用的发展。由于边缘设备采集能力有限，大量轨迹以低采样率记录，可能导致城市应用的有效性下降。本文旨在基于低采样轨迹在无路网信息的自由空间中恢复出高采样轨迹，以提升轨迹数据的可用性并更有效地支持城市应用。近期针对轨迹恢复的研究通常假设轨迹集中在中心位置，无法处理去中心化轨迹且存在隐私风险。为弥合去中心化训练与轨迹恢复之间的鸿沟，我们提出一种基于客户端-服务器架构的轻量级框架LightTR，用于联邦轨迹恢复，同时确保各客户端/平台中心（如公司数据中心）的轨迹数据保持去中心化与隐私性。具体而言，考虑到边缘设备的有限处理能力，LightTR包含一个轻量本地轨迹嵌入模块，在不牺牲特征提取能力的前提下提升计算效率。该框架还设计了元知识增强的本地-全局训练方案，以降低服务器与客户端之间的通信成本，从而进一步优化效率。大量实验证明了所提框架的有效性与高效性。