智能建筑异常检测的联邦学习方法

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Internet of Things Pub Date : 2020-10-20 DOI:10.1145/3467981
Raed Abdel Sater, A. Hamza
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引用次数: 51

摘要

智能建筑中的物联网(IoT)传感器变得越来越普遍,使建筑更加宜居、节能和可持续。这些设备感知环境并生成多元时间数据,这些数据对于检测异常和改进智能建筑中的能源使用预测至关重要。然而,在集中式系统中检测这些异常通常会受到响应时间的巨大延迟的困扰。为了克服这一问题,我们利用多任务学习范式在联邦学习环境中制定异常检测问题,该范式旨在同时解决多个任务,同时利用任务之间的相似性和差异性。我们提出了一种使用堆叠长短时记忆(LSTM)模型的新型隐私设计联邦学习模型,并且我们证明了与集中式LSTM相比,它在训练收敛期间的速度要快两倍以上。我们的联合学习方法的有效性在通用电气当前智能建筑的物联网生产系统生成的三个真实数据集上得到了证明,与分类和回归任务的基线方法相比,实现了最先进的性能。我们的实验结果证明了该框架在不影响预测性能的情况下降低整体训练成本的有效性。
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A Federated Learning Approach to Anomaly Detection in Smart Buildings
Internet of Things (IoT) sensors in smart buildings are becoming increasingly ubiquitous, making buildings more livable, energy efficient, and sustainable. These devices sense the environment and generate multivariate temporal data of paramount importance for detecting anomalies and improving the prediction of energy usage in smart buildings. However, detecting these anomalies in centralized systems is often plagued by a huge delay in response time. To overcome this issue, we formulate the anomaly detection problem in a federated learning setting by leveraging the multi-task learning paradigm, which aims at solving multiple tasks simultaneously while taking advantage of the similarities and differences across tasks. We propose a novel privacy-by-design federated learning model using a stacked long short-time memory (LSTM) model, and we demonstrate that it is more than twice as fast during training convergence compared to the centralized LSTM. The effectiveness of our federated learning approach is demonstrated on three real-world datasets generated by the IoT production system at General Electric Current smart building, achieving state-of-the-art performance compared to baseline methods in both classification and regression tasks. Our experimental results demonstrate the effectiveness of the proposed framework in reducing the overall training cost without compromising the prediction performance.
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来源期刊
CiteScore
5.20
自引率
3.70%
发文量
0
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