FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learning

TL;DR

FedTDP is a novel federated learning framework that enhances trajectory data quality while preserving privacy, utilizing LLMs and innovative optimization techniques to outperform existing methods across multiple datasets and tasks.

Contribution

The paper introduces FedTDP, a unified, privacy-preserving framework for trajectory data preparation that leverages LLMs and federated optimization, addressing privacy and generalizability issues.

Findings

Outperforms 13 state-of-the-art baselines.

Effective across 6 real datasets and 10 TDP tasks.

Reduces data transmission and accelerates training.

Abstract

Trajectory data, which capture the movement patterns of people and vehicles over time and space, are crucial for applications like traffic optimization and urban planning. However, issues such as noise and incompleteness often compromise data quality, leading to inaccurate trajectory analyses and limiting the potential of these applications. While Trajectory Data Preparation (TDP) can enhance data quality, existing methods suffer from two key limitations: (i) they do not address data privacy concerns, particularly in federated settings where trajectory data sharing is prohibited, and (ii) they typically design task-specific models that lack generalizability across diverse TDP scenarios. To overcome these challenges, we propose FedTDP, a privacy-preserving and unified framework that leverages the capabilities of Large Language Models (LLMs) for TDP in federated environments. Specifically, we: (i) design a trajectory privacy autoencoder to secure data transmission and protect privacy, (ii) introduce a trajectory knowledge enhancer to improve model learning of TDP-related knowledge, enabling the development of TDP-oriented LLMs, and (iii) propose federated parallel optimization to enhance training efficiency by reducing data transmission and enabling parallel model training. Experiments on 6 real datasets and 10 mainstream TDP tasks demonstrate that FedTDP consistently outperforms 13 state-of-the-art baselines.