# Guardians of the Deep Fog: Failure-Resilient DNN Inference from Edge to Cloud

arXiv: https://arxiv.org/abs/1909.00995 · HTML: https://mlsanity.com/paper/1909.00995/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2019.

## Built on by (most cited first, 8 of 8)

- [A Survey on Collaborative DNN Inference for Edge Intelligence](https://mlsanity.com/paper/2207.07812/) (2022)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Multi-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge Learning](https://mlsanity.com/paper/2205.11854/) (2022)
- [Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems](https://mlsanity.com/paper/2404.13348/) (2024)
- [Privacy-Aware Joint DNN Model Deployment and Partitioning Optimization for Collaborative Edge Inference Services](https://mlsanity.com/paper/2502.16091/) (2025)
- [CONTINUER: Maintaining Distributed DNN Services During Edge Failures](https://mlsanity.com/paper/2206.05267/) (2022)
- [MEL: Multi-level Ensemble Learning for Resource-Constrained Environments](https://mlsanity.com/paper/2506.20094/) (2025)
- [Adaptive ResNet Architecture for Distributed Inference in Resource-Constrained IoT Systems](https://mlsanity.com/paper/2307.11499/) (2023)

## Abstract (arXiv)

Partitioning and distributing deep neural networks (DNNs) over physical nodes such as edge, fog, or cloud nodes, could enhance sensor fusion, and reduce bandwidth and inference latency. However, when a DNN is distributed over physical nodes, failure of the physical nodes causes the failure of the DNN units that are placed on these nodes. The performance of the inference task will be unpredictable, and most likely, poor, if the distributed DNN is not specifically designed and properly trained for failures. Motivated by this, we introduce deepFogGuard, a DNN architecture augmentation scheme for making the distributed DNN inference task failure-resilient. To articulate deepFogGuard, we introduce the elements and a model for the resiliency of distributed DNN inference. Inspired by the concept of residual connections in DNNs, we introduce skip hyperconnections in distributed DNNs, which are the basis of deepFogGuard's design to provide resiliency. Next, our extensive experiments using two existing datasets for the sensing and vision applications confirm the ability of deepFogGuard to provide resiliency for distributed DNNs in edge-cloud networks.