Inference Offloading for Cost-Sensitive Binary Classification at the Edge
Vishnu Narayanan Moothedath, Umang Agarwal, Umeshraja N, James Richard Gross, Jaya Prakash Champati, Sharayu Moharir
Abstract
We investigate a binary classification problem in an edge intelligence system where false negatives are more costly than false positives. The system features a compact, locally deployed model, supplemented by a larger, remote model that is accessible via the network, albeit at an offloading cost. For each sample, our system first uses the locally deployed model for inference. Based on the output of the local model, the sample may be offloaded to the remote model. This work aims to understand the fundamental trade-off between classification accuracy and the offloading costs within such a hierarchical inference (HI) system. To optimise this system, we propose an online learning framework that continuously adapts a pair of thresholds on the local model
BibTeX
@inproceedings{aaai2026_inferenceoffload,
title = {Inference Offloading for Cost-Sensitive Binary Classification at the Edge},
author = {Vishnu Narayanan Moothedath and Umang Agarwal and Umeshraja N and James Richard Gross and Jaya Prakash Champati and Sharayu Moharir},
booktitle = {AAAI 2026},
year = {2026}
}