Privacy-preserving federated learning for proactive maintenance of IoT-empowered multi-location smart city facilities

Abstract

The widespread adoption of the Internet of Things (IoT) and deep learning (DL) have facilitated a social paradigm shift towards smart cities, accelerating the rapid construction of smart facilities. However, newly constructed facilities often lack the necessary data to learn any predictive models, preventing them from being truly smart. Additionally, data collected from different facilities is heterogeneous or may even be privacy-sensitive, making it harder to train proactive maintenance management (PMM) models that are robust to provide services across them. These properties impose challenges that have not been adequately addressed, especially at the city level. In this paper, we present a privacy-preserving, federated learning (FL) framework that can assist management personnel to proactively manage the maintenance schedule of IoT-empowered facilities in different organizations through analyzing heterogeneous IoT data. Our framework consists of (1) an FL platform implemented with fully homomorphic encryption (FHE) for training DL models with time-series heterogeneous IoT data and (2) an FL-based long short-term memory autoencoder model, namely FedLSTMA, for facility-level PMM. To evaluate our framework, we did extensive simulations with real-world data harvested from IoT-empowered public toilets, demonstrating that the DL-based FedLSTMA outperformed other traditional machine learning (ML) algorithms and had a high level of generalizability and capabilities of transferring knowledge from existing facilities to newly constructed facilities under the situation of huge data heterogeneity. We believe that our framework can be a potential solution for overcoming the challenges inherent in managing and maintaining other smart facilities, ultimately contributing to the effective realization of smart cities.