# Efficient Feature Compression for Edge-Cloud Systems

Source: [Efficient Feature Compression for Edge-Cloud Systems](<https://www.sota2.com/research/paper/68fa7fcc1e209131cea9b162>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T06:52:33.836Z

Authors: Zhihao Duan, Fengqing Zhu

Published: 2022-11-17T21:23:35.000Z

## Abstract

Optimizing computation in an edge-cloud system is an important yet challenging problem. In this paper, we consider a three-way trade-off between bit rate, classification accuracy, and encoding complexity in an edge-cloud image classification system. Our method includes a new training strategy and an efficient encoder architecture to improve the rate-accuracy performance. Our design can also be easily scaled according to different computation resources on the edge device, taking a step towards achieving a rate-accuracy-complexity (RAC) trade-off. Under various settings, our feature coding system consistently outperforms previous methods in terms of the RAC performance.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2211.09897>)

[PDF](<https://arxiv.org/pdf/2211.09897.pdf>)