LightTR: A Lightweight Framework for Federated Trajectory Recovery

TL;DR

LightTR is a lightweight federated framework designed to recover high-sampled trajectories from low-sampled data on edge devices, enhancing urban applications while preserving data privacy and reducing communication costs.

Contribution

The paper introduces LightTR, a novel federated trajectory recovery framework that is efficient, privacy-preserving, and suitable for edge devices with limited processing power.

Findings

LightTR achieves high recovery accuracy in experiments.

The framework significantly reduces communication costs.

It maintains data privacy across decentralized clients.

Abstract

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.