Federated Latent Factor Learning for Recovering Wireless Sensor Networks Signal with Privacy-Preserving
Chengjun Yu, Yixin Ran, Yangyi Xia, Jia Wu, Xiaojing Liu
TL;DR
This work tackles missing data in Wireless Sensor Networks under privacy constraints by introducing FLFL-SSR, a federated latent factor learning framework that preserves privacy by exchanging gradient updates rather than raw data. It combines sensor-level vertical FL with a local spatial sharing strategy, and enforces spatial smoothness via region-specific Laplacian regularization, leveraging the low-rank structure Y ≈ PQ^T. The method demonstrates superior recovery accuracy over several federated baselines on two real-world WSN datasets, validating the importance of incorporating spatial correlations in FL for WSN data completion. The results suggest FLFL-SSR's practical potential for privacy-preserving data recovery in distributed sensing systems, with future work aimed at improving robustness to outliers.
Abstract
Wireless Sensor Networks (WSNs) are a cutting-edge domain in the field of intelligent sensing. Due to sensor failures and energy-saving strategies, the collected data often have massive missing data, hindering subsequent analysis and decision-making. Although Latent Factor Learning (LFL) has been proven effective in recovering missing data, it fails to sufficiently consider data privacy protection. To address this issue, this paper innovatively proposes a federated latent factor learning (FLFL) based spatial signal recovery (SSR) model, named FLFL-SSR. Its main idea is two-fold: 1) it designs a sensor-level federated learning framework, where each sensor uploads only gradient updates instead of raw data to optimize the global model, and 2) it proposes a local spatial sharing strategy, allowing sensors within the same spatial region to share their latent feature vectors, capturing spatial correlations and enhancing recovery accuracy. Experimental results on two real-world WSNs datasets demonstrate that the proposed model outperforms existing federated methods in terms of recovery performance.