# DeepFT: Fault-Tolerant Edge Computing using a Self-Supervised Deep Surrogate Model

arXiv: https://arxiv.org/abs/2212.01302 · HTML: https://mlsanity.com/paper/2212.01302/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 54 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Gated Graph Recurrent Neural Networks](https://mlsanity.com/paper/2002.01038/) (2020)
- [An introduction to Docker for reproducible research, with examples from the R environment](https://mlsanity.com/paper/1410.0846/) (2014)
- [Self-Supervised Prototypical Transfer Learning for Few-Shot Classification](https://mlsanity.com/paper/2006.11325/) (2020)
- [COSCO: Container Orchestration using Co-Simulation and Gradient Based Optimization for Fog Computing Environments](https://mlsanity.com/paper/2104.14392/) (2021)
- [PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computing](https://mlsanity.com/paper/2112.02292/) (2021)
- [DeFog: Fog Computing Benchmarks](https://mlsanity.com/paper/1907.10890/) (2019)
- [Dependability in Edge Computing](https://mlsanity.com/paper/1710.11222/) (2017)
- [CEFIoT: A Fault-Tolerant IoT Architecture for Edge and Cloud](https://mlsanity.com/paper/2001.08433/) (2020)
- [DRAGON: Decentralized Fault Tolerance in Edge Federations](https://mlsanity.com/paper/2208.07658/) (2022)

## Built on by (most cited first, 2 of 2)

- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [Adaptive NAD: Online and Self-adaptive Unsupervised Network Anomaly Detector](https://mlsanity.com/paper/2410.22967/) (2024)

## Abstract (arXiv)

The emergence of latency-critical AI applications has been supported by the evolution of the edge computing paradigm. However, edge solutions are typically resource-constrained, posing reliability challenges due to heightened contention for compute and communication capacities and faulty application behavior in the presence of overload conditions. Although a large amount of generated log data can be mined for fault prediction, labeling this data for training is a manual process and thus a limiting factor for automation. Due to this, many companies resort to unsupervised fault-tolerance models. Yet, failure models of this kind can incur a loss of accuracy when they need to adapt to non-stationary workloads and diverse host characteristics. To cope with this, we propose a novel modeling approach, called DeepFT, to proactively avoid system overloads and their adverse effects by optimizing the task scheduling and migration decisions. DeepFT uses a deep surrogate model to accurately predict and diagnose faults in the system and co-simulation based self-supervised learning to dynamically adapt the model in volatile settings. It offers a highly scalable solution as the model size scales by only 3 and 1 percent per unit increase in the number of active tasks and hosts. Extensive experimentation on a Raspberry-Pi based edge cluster with DeFog benchmarks shows that DeepFT can outperform state-of-the-art baseline methods in fault-detection and QoS metrics. Specifically, DeepFT gives the highest F1 scores for fault-detection, reducing service deadline violations by up to 37% while also improving response time by up to 9%.