The continued development of neural network architectures continues to drive demand for computing power. While data center scaling continues, inference away from the cloud will increasingly rely on distributed inference on multiple devices. Most …
The deployment of increasingly complex deep learn- ing models for inference in real world settings requires dealing with the constrained computational capabilities of edge devices. Splitting inference between edge and cloud has been proposed to …
The rapid growth of connected sensing devices has resulted in enormous amounts of data being collected and processed. Air quality data collected from different monitoring stations is spatially and temporally correlated, and hence, collaborative …
Low-cost air quality monitoring devices can provide high-density spatiotemporal pollution data, thus offering a better opportunity to apply machine learning (ML). Low-cost sensor nodes usually utilize microcontrollers as the main processors, and …
Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing privacy and reducing communication. However, it presents numerous challenges relating to the heterogeneity of the data distribution, device …
Long-Short Term Memory (LSTM) networks, and Recurrent Neural Networks (RNNs) in general, have demonstrated their suitability in many time series data applications, especially in Natural Language Pro- cessing (NLP). Computationally, LSTMs introduce …
A key challenge in building machine learning models for time series prediction is the incompleteness of the datasets. Missing data can arise for a variety of reasons, including sensor failure and network outages, resulting in datasets that can be …
Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In …