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.

基于轻量级的客户端 - 服务器架构，充分利用轨迹数据，提供轨迹恢复和数据隐私保护的轻量级框架，旨在提高城市应用的效能。