<i>ShieldTSE</i>: A Privacy-Enhanced Split Federated Learning Framework for Traffic State Estimation in IoV

Traffic state estimation (TSE) is attracting significant attention due to its importance to the Internet of Vehicles (IoV) for various applications, such as vehicle path planning. In classic IoV, the real-time traffic data collected by road side units requires transferring to the cloud server for processing. Such a centralized manner may raise privacy leakage issues. Split federated learning (SFL) has emerged as one of the prevalent methods to solve these issues. However, recent studies have shown that the existing SFL frameworks are vulnerable to the model inversion (MI) attacks, leading to private raw data leakage. To this end, in this article, we propose ShieldTSE, a privacy-enhanced SFL framework for TSE in IoV. To protect privacy and maintain utility, a variational encoder-decoder-based privacy-preserving feature extraction module with adversarial learning is first proposed to generate better privacy-preserved intermediate activations with a lower-dimensional feature space. Then, a hard attention-based feature selection module is designed to select partial yet crucial features from the intermediate activations by removing redundant sensitive features to further reduce the data privacy leakage. Experimental results demonstrate that ShieldTSE achieves superior privacy-preserving ability when against training-based and optimization-based MI attacks with an average reconstruction mean-square error (MSE) improvement of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$18\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$35\times $ </tex-math></inline-formula> on METR-LA and PEMS-BAY compared to the baseline without the privacy-preserving strategy, respectively. ShieldTSE also successfully maintains better model utility compared to the privacy protection baselines.