  The vision of pervasive machine learning (ML) services can be realized by
training an ML model on time using real-time data collected by internet of
things (IoT) devices. To this end, IoT devices require offloading their data to
an edge server in proximity. On the other hand, high dimensional data with a
heavy volume causes a significant burden to an IoT device with a limited energy
budget. To cope with the limitation, we propose a novel offloading
architecture, called joint data deepening and prefetching (JD2P), which is
feature-by-feature offloading comprising two key techniques. The first one is
data deepening, where each data sample's features are sequentially offloaded in
the order of importance determined by the data embedding technique such as
principle component analysis (PCA). No more features are offloaded when the
features offloaded so far are enough to classify the data, resulting in
reducing the amount of offloaded data. The second one is data prefetching,
where some features potentially required in the future are offloaded in
advance, thus achieving high efficiency via precise prediction and parameter
optimization. To verify the effectiveness of JD2P, we conduct experiments using
the MNIST and fashion-MNIST dataset. Experimental results demonstrate that the
JD2P can significantly reduce the expected energy consumption compared with
several benchmarks without degrading learning accuracy.
