Privacy-Preserving Sparse Traffic Flow Prediction in IIoT: A Three-Tier Federated Learning Framework

Traffic flow prediction, as a typical application of Industrial Internet of Things (IIoT) in urban infrastructure, faces critical security challenges. Existing privacy-preserving methods in two-tier federated learning (FL) frameworks primarily focus on dense data while neglecting privacy vulnerabilities in massive sparse traffic flow collected by clients, failing to effectively protect both high-sparsity traffic flow and federated pretrained models against privacy leakage risks. Therefore, this article proposes a novel three-tier FL framework-based privacy-preserving sparse traffic flow prediction (TFLST) scheme, achieving dual protection of sparse traffic flow and model parameters with high-precision prediction. Specifically, we innovatively design a spatiotemporal self-attention transformer-based Gestalt sparse key cell selection (STGSC) method to efficiently extract sparse key cells with high spatiotemporal correlations. Additionally, an adaptive truncated Gaussian mechanism-based local sparse traffic flow protection (ATLSP) algorithm is proposed, which dynamically allocates privacy budgets according to sparse correlations to achieve high-utility sparse data protection. A dynamic spatiotemporal matrix completion-based GCN pretraining protection (DSMGP) method is adopted to enhance the spatiotemporal features of sparse data efficiently, protect model parameter privacy, and improve FL training accuracy. Subsequently, we introduce a spatiotemporal self-supervised learning-based multiobjective weighted traffic flow prediction (SMWTP) method to achieve high-accuracy traffic flow prediction. Rigorous security analysis proves that our scheme satisfies differential privacy requirements. Experimental results on four real-world datasets show that our TFLST scheme reduces prediction errors by 6.21% compared to state-of-the-art methods, effectively balancing data privacy and utility.