Grad-FEC: Unequal Loss Protection of Deep Features in Collaborative Intelligence

Korcan Uyanik, S. Faegheh Yeganli, Ivan V. Bajić
7/4/2023
eess.IVcs.CV

No confident code implementation found for this paper.

Abstract

Collaborative intelligence (CI) involves dividing an artificial intelligence (AI) model into two parts: front-end, to be deployed on an edge device, and back-end, to be deployed in the cloud. The deep feature tensors produced by the front-end are transmitted to the cloud through a communication channel, which may be subject to packet loss. To address this issue, in this paper, we propose a novel approach to enhance the resilience of the CI system in the presence of packet loss through Unequal Loss Protection (ULP). The proposed ULP approach involves a feature importance estimator, which estimates the importance of feature packets produced by the front-end, and then selectively applies Forward Error Correction (FEC) codes to protect important packets. Experimental results demonstrate that the proposed approach can significantly improve the reliability and robustness of the CI system in the presence of packet loss.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

LR-V2X: Loss-Resilient Collaborative Perception under Low-Bandwidth Communication

79% match1 repo2026

Generative Feature Imputing -- A Technique for Error-resilient Semantic Communication

78% match2025

Conquering High Packet-Loss Erasure: MoE Swin Transformer-Based Video Semantic Communication

77% match2025

Entropy Loss: An Interpretability Amplifier of 3D Object Detection Network for Intelligent Driving

76% match1 repo12024

Packet-Loss Robust 3D Gaussian Compression via Atomic Packaging and GNN-based Error Concealment

76% match2026

Cyber-Resilient Digital Twins: Discriminating Attacks for Safe Critical Infrastructure Control

75% match2026

Cite this paper

@article{uyanik2023gradfec,
  title  = {Grad-FEC: Unequal Loss Protection of Deep Features in Collaborative Intelligence},
  author = {Korcan Uyanik and S. Faegheh Yeganli and Ivan V. Bajić},
  year   = {2023},
  eprint = {2307.01846},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.01846v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2307.01846.svg)](https://codeofpaper.com/paper/2307.01846)

Discussion