In this thesis, we take a significant step towards smart healthcare by leveraging the strengths of WMSs and efficient machine learning. We first propose a novel healthcare framework called the Health Decision Support System (HDSS) that enables disease diagnosis in both in- and out-of-clinic scenarios through the integration of WMS data with clinical decision support systems (CDSSs). We further evaluate the effectiveness of the WMS based disease diagnosis through DiabDeep – a framework that combines off-the-shelf WMSs and efficient neural networks for pervasive diabetes diagnosis.
To effectively monitor 69,000 human diseases on the edge, one has to ensure efficiency of inference given very limited communication bandwidth, sensor energy/storage, and training data accessibility upon model deployment. To solve these challenges, we further focus on efficient inference from three different perspectives:
• Communication efficiency. We first study how smart and conventional sensors can work collaboratively along the IoT hierarchy for efficient inference. We propose a novel hierarchical inference model based on hierarchical learning and local inferences. We show that for seven applications, the proposed method helps reduce the amount of transmission by up to 60x against conventional baselines, while improving accuracy.
• Model efficiency. We next focus on improving the efficiency of the inference model. We propose a hardware-guided symbiotic training methodology for compact, execution-efficient, yet accurate neural networks. For two well-known long short-term memory (LSTM) applications, we have achieved 7-31x parameter reduction, 1.7-5.2x latency reduction, while improving accuracy.
• Data efficiency. One fundamental assumption made by existing methods for efficient inference, including the aforementioned methods, relies on access to original training data. While entities might want to share their models, the training datasets are not only large but difficult to store, transfer, and manage. To tackle this, we enable knowledge transfer from a trained convolutional neural network (CNN) to accommodate new design tasks without using the original data. We introduce DeepInversion, a method that converts random noise into high-fidelity class-conditional images given just a pretrained CNN classifier. We demonstrate its applicability to three tasks of immense practical importance – (i) data-free network pruning, (ii) data-free knowledge transfer, and (iii) data-free continual learning.