Fed-SecTP: A Federated Learning-Based Framework for Secure Vehicle Trajectory Prediction Using Surrounding Vehicle Data

Accurate vehicle trajectory prediction process depends on seamless data sharing within the Internet of Vehicles. However, such interconnected data exchange introduces significant security risks. Specifically, network attacks can compromise data integrity, thereby degrading prediction accuracy. Concurrently, the need to protect sensitive vehicle data, such as driving trajectories and user account information, results in data silos that hinder the free flow of information essential for effective prediction. Existing studies have largely addressed either privacy preservation or attack mitigation in isolation, lacking a unified solution that simultaneously tackles both challenges. To address this gap, we propose Fed-SecTP, an integrated dual-module secure federated learning framework. The first module employs a Temporal Convolutional Network (TCN) with multi-head attention to detect and filter network attacks in real-time. The second module combines TCN with a Bidirectional Long Short-Term Memory (Bi-LSTM) network for trajectory prediction and leverages FedProx for federated learning, thereby enabling privacy-preserving model training without sharing raw data. Experimental results demonstrate that Fed-SecTP achieves high prediction accuracy and robustness even when up to 50% of the data is compromised by attacks, while ensuring secure data processing. This framework offers a reliable and comprehensive solution for autonomous vehicle trajectory prediction.