FLFL: Federated Latent Factor Learning for Private Recovery of Spatio-Temporal Signals
2026-06-22 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors address the problem of missing data in wireless sensor networks caused by sensor failures or shutdowns. They propose a new model called federated latent factor learning (FLFL) that helps recover this missing data without sharing the raw data, protecting privacy. Their method uses a decentralized approach where sensors only share gradient information, and it leverages the natural patterns in space and time to improve accuracy. Experiments on real datasets show that their approach works better than other existing methods while keeping data private.
Wireless Sensor NetworksMissing Data RecoveryLatent Factor LearningFederated LearningSpatio-temporal CorrelationPrivacy PreservationGradient SharingDecentralized TrainingSignal Recovery
Authors
Chengjun Yu, Di Wu, Yi He, Jia Chen
Abstract
Wireless sensor network (WSNs) stands out as a burgeoning and promising domain in intelligent sensing. Owing to various factors such as sudden sensor malfunctions or deliberate shutdown of partial nodes to save energy, the collected sensing signals from WSNs commonly have massive missing data, leading to adverse effects on subsequent analysis or decision-making. Latent factor learning (LFL) has proven to be highly effective in recovering the missing data for WSNs. However, the existing LFL models require the collected sensing signals to be maintained in one central place like a central server, which is becoming unacceptable for data owners who are getting increasingly privacy-sensitive. To address this issue, this paper innovatively proposes a federated latent factor learning (FLFL) model for privacy-preserving spatio-temporal signal recovery. Its main idea is two-fold: 1) it designs a sensor-level federated learning framework based on LFL, where each sensor only needs to upload gradient information rather than raw data for training a privacy-preserving recovery model, and 2) it incorporates the spatio-temporal correlation into the designed federated learning framework as the regularization constraint to improve its recovery accuracy. With such designs, FLFL can not only accurately recover the missing data of WSNs but also ensure data owners' privacy-preserving of raw data. To evaluate the proposed FLFL model, extensive experiments have been conducted on four real-world WSN datasets. The results demonstrate that FLFL significantly outperforms eight state-of-the-art federated and non-federated signal recovery models in terms of recovery accuracy with privacy-preserving.