LightTR: A Lightweight Framework for Federated Trajectory Recovery

• Main Authors: Liu, Ziqiao, Miao, Hao, Zhao, Yan, Liu, Chenxi, Zheng, Kai, Li, Huan
• Format: Conference Proceeding
• Language: English
• Subjects: Computational efficiency; Feature extraction; Federated Learning; Lightweight; Privacy; Roads; Servers; Training; Trajectory; Trajectory Recovery
• ISSN: 2375-026X
• DOI: 10.1109/ICDE60146.2024.00337

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.