  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.
