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.

中文翻译：

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保护隐私的联合学习，用于主动维护物联网支持的多地点智慧城市设施


物联网（IoT）和深度学习（DL）的广泛应用促进了社会范式向智慧城市的转变，加速了智能设施的快速建设。然而，新建的设施通常缺乏学习任何预测模型所需的数据，从而阻碍了它们真正的智能。此外，从不同设施收集的数据是异构的，甚至可能是隐私敏感的，这使得训练主动维护管理（PMM）模型变得更加困难，该模型能够稳健地跨设施提供服务。这些特性带来的挑战尚未得到充分解决，特别是在城市层面。在本文中，我们提出了一种保护隐私的联邦学习（FL）框架，可以帮助管理人员通过分析异构物联网数据主动管理不同组织中物联网支持设施的维护计划。我们的框架包括（1）一个采用完全同态加密（FHE）实现的 FL 平台，用于使用时间序列异构物联网数据训练深度学习模型，以及（2）一个基于 FL 的长短期记忆自动编码器模型，即 FedLSTMA，用于设施级PMM。为了评估我们的框架，我们对从物联网公共厕所收集的真实数据进行了广泛的模拟，证明基于深度学习的 FedLSTMA 优于其他传统机器学习 (ML) 算法，并且具有高水平的通用性和知识转移能力数据异构性巨大的情况下从现有设施到新建设施。 我们相信，我们的框架可以成为克服管理和维护其他智能设施固有挑战的潜在解决方案，最终有助于有效实现智慧城市。