Collaborative Inference for Efficient Remote Monitoring
- URL: http://arxiv.org/abs/2002.04759v1
- Date: Wed, 12 Feb 2020 01:57:17 GMT
- Title: Collaborative Inference for Efficient Remote Monitoring
- Authors: Chi Zhang, Yong Sheng Soh, Ling Feng, Tianyi Zhou, Qianxiao Li
- Abstract summary: A naive approach to resolve this on the model level is to use simpler architectures.
We propose an alternative solution by decomposing the predictive model as the sum of a simple function which serves as a local monitoring tool.
A sign requirement is imposed on the latter to ensure that the local monitoring function is safe.
- Score: 34.27630312942825
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While current machine learning models have impressive performance over a wide
range of applications, their large size and complexity render them unsuitable
for tasks such as remote monitoring on edge devices with limited storage and
computational power. A naive approach to resolve this on the model level is to
use simpler architectures, but this sacrifices prediction accuracy and is
unsuitable for monitoring applications requiring accurate detection of the
onset of adverse events. In this paper, we propose an alternative solution to
this problem by decomposing the predictive model as the sum of a simple
function which serves as a local monitoring tool, and a complex correction term
to be evaluated on the server. A sign requirement is imposed on the latter to
ensure that the local monitoring function is safe, in the sense that it can
effectively serve as an early warning system. Our analysis quantifies the
trade-offs between model complexity and performance, and serves as a guidance
for architecture design. We validate our proposed framework on a series of
monitoring experiments, where we succeed at learning monitoring models with
significantly reduced complexity that minimally violate the safety requirement.
More broadly, our framework is useful for learning classifiers in applications
where false negatives are significantly more costly compared to false
positives.
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