# A Federated Learning Approach to Anomaly Detection in Smart Buildings

arXiv: https://arxiv.org/abs/2010.10293 · HTML: https://mlsanity.com/paper/2010.10293/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 17 of 17)

- [A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems](https://mlsanity.com/paper/2206.04149/) (2022)
- [Fast Deep Autoencoder for Federated learning](https://mlsanity.com/paper/2206.05136/) (2022)
- [A Review of Federated Learning in Energy Systems](https://mlsanity.com/paper/2208.10941/) (2022)
- [Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives](https://mlsanity.com/paper/2304.08602/) (2023)
- [Collaborative Drug Discovery: Inference-level Data Protection Perspective](https://mlsanity.com/paper/2205.06506/) (2022)
- [A federated large language model for long-term time series forecasting](https://mlsanity.com/paper/2407.20503/) (2024)
- [Deep Federated Anomaly Detection for Multivariate Time Series Data](https://mlsanity.com/paper/2205.04041/) (2022)
- [A review of federated learning in renewable energy applications: Potential, challenges, and future directions](https://mlsanity.com/paper/2312.11220/) (2023)
- [Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data](https://mlsanity.com/paper/2404.14933/) (2024)
- [Suppressing Noise from Built Environment Datasets to Reduce Communication Rounds for Convergence of Federated Learning](https://mlsanity.com/paper/2209.01417/) (2022)
- [Support Vector Based Anomaly Detection in Federated Learning](https://mlsanity.com/paper/2407.03920/) (2024)
- [Detection of Global Anomalies on Distributed IoT Edges with Device-to-Device Communication](https://mlsanity.com/paper/2407.11308/) (2024)
- [Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis](https://mlsanity.com/paper/2411.03996/) (2024)
- [Long-term Detection System for Six Kinds of Abnormal Behavior of the Elderly Living Alone](https://mlsanity.com/paper/2411.13153/) (2024)
- [Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks](https://mlsanity.com/paper/2502.05041/) (2025)
- [Clustered Federated Learning for Generalizable FDIA Detection in Smart Grids with Heterogeneous Data](https://mlsanity.com/paper/2507.14999/) (2025)
- [From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0](https://mlsanity.com/paper/2507.15796/) (2025)

## Abstract (arXiv)

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.