Federated Latent Factor Learning for Recovering Wireless Sensor Networks Signal with Privacy-Preserving
It addresses data recovery with privacy protection for WSNs, an incremental improvement over existing federated methods.
This paper tackles the problem of recovering missing data in Wireless Sensor Networks (WSNs) while preserving privacy by proposing a federated latent factor learning model (FLFL-SSR), which outperforms existing federated methods in recovery performance on real-world datasets.
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.