Context-aware convolutional neural network over distributed system in collaborative computing

As the computing power of end-point devices grows, there has been interest in developing distributed deep neural networks specifically for hierarchical inference deployments on multi-sensor systems. However, as the existing approaches rely on latent parameters trained by machine learning, it is difficult to preemptively select front-end deep features across sensors, or understand individual feature's relative importance for systematic global inference. In this paper, we propose multi-view convolutional neural networks exploiting likelihood estimation. Proof-of-concept experiments show that our likelihood-based context selection and weighted averaging collaboration scheme can decrease an endpoint's communication and energy costs by a factor of 3×, while achieving high accuracy comparable to the original aggregation approaches.