Federated Spatiotemporally Regularized Latent Factorization of Tensors for Privacy-Preserving Multi-Indicator Data Recovery in Wireless Sensor Networks

Wireless sensor networks (WSNs) continuously collect multivariate environmental data, yet sensor failures, energy constraints, and unreliable communication frequently lead to substantial missing values. Latent factor analysis (LFA) is a representative approach to extract compact latent representations from irregular environmental sensing data, providing informative features for downstream data recovery. However, existing LFA methods typically model each sensing indicator independently, overlooking the inherent spatiotemporal correlations across indicators in sensing data. Moreover, they require raw data to be aggregated at a central server, conflicting with increasingly stringent privacy-preserving requirements. To solve these issues, this paper proposes a federated spatiotemporally-regularized latent factorization of tensors (FedSR-LFT) model. It builds on two-fold key ideas: 1) developing a sensor-level federated latent factorization of tensors model that jointly models multiple sensing indicators and supports privacy-preserving collaborative training by exchanging only gradient information while keeping raw data local, and 2) incorporating spatiotemporal regularization and learnable indicator-loss weight to enrich latent spatiotemporal information and adaptively adjust each indicator's influence, thereby enabling accurate multi-indicator data recovery. Together, these designs enable FedSR-LFT to exploit cross-indicator dependencies for accurate WSN data recovery with privacy preservation. Extensive experiments on two real-world WSN datasets are conducted to compare FedSR-LFT with eight state-of-the-art federated signal recovery baselines. The results show that FedSR-LFT significantly outperforms all baselines in recovery accuracy while preserving data privacy. The source code and datasets are submitted as Supplementary Materials for Reproducibility.