Federated Multi-task Hierarchical Attention Model for Sensor Analytics
This addresses data scarcity in sensor analytics for IoT applications, but it is incremental as it builds on existing federated and attention-based methods.
The paper tackles the problem of scarce training data per sensor in IoT applications by proposing FATHOM, a federated multi-task hierarchical attention model that jointly trains classification/regression models from multiple sensors, outperforming competitive baselines in activity recognition and environment monitoring datasets.
Sensors are an integral part of modern Internet of Things (IoT) applications. There is a critical need for the analysis of heterogeneous multivariate temporal data obtained from the individual sensors of these systems. In this paper we particularly focus on the problem of the scarce amount of training data available per sensor. We propose a novel federated multi-task hierarchical attention model (FATHOM) that jointly trains classification/regression models from multiple sensors. The attention mechanism of the proposed model seeks to extract feature representations from the input and learn a shared representation focused on time dimensions across multiple sensors. The underlying temporal and non-linear relationships are modeled using a combination of attention mechanism and long-short term memory (LSTM) networks. We find that our proposed method outperforms a wide range of competitive baselines in both classification and regression settings on activity recognition and environment monitoring datasets. We further provide visualization of feature representations learned by our model at the input sensor level and central time level.