# Deep Learning in the Era of Edge Computing: Challenges and Opportunities

arXiv: https://arxiv.org/abs/2010.08861 · HTML: https://mlsanity.com/paper/2010.08861/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [Federated Learning for Internet of Things: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2111.07494/) (2021)
- [DeepEdgeBench: Benchmarking Deep Neural Networks on Edge Devices](https://mlsanity.com/paper/2108.09457/) (2021)
- [When in Doubt, Summon the Titans: Efficient Inference with Large Models](https://mlsanity.com/paper/2110.10305/) (2021)
- [FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective](https://mlsanity.com/paper/2110.03061/) (2021)
- [Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review](https://mlsanity.com/paper/2205.01081/) (2022)
- [Towards Optimal Compression: Joint Pruning and Quantization](https://mlsanity.com/paper/2302.07612/) (2023)
- [Federated Learning Hyper-Parameter Tuning from a System Perspective](https://mlsanity.com/paper/2211.13656/) (2022)
- [From MNIST to ImageNet: Understanding the Scalability Boundaries of Differentiable Logic Gate Networks](https://mlsanity.com/paper/2509.25933/) (2025)
- [Knowledge Distillation Neural Network for Predicting Car-following Behaviour of Human-driven and Autonomous Vehicles](https://mlsanity.com/paper/2411.05618/) (2024)
- [E2CAR: An Efficient 2D-CNN Framework for Real-Time EEG Artifact Removal on Edge Devices](https://mlsanity.com/paper/2602.09035/) (2026)

## Abstract (arXiv)

The era of edge computing has arrived. Although the Internet is the backbone of edge computing, its true value lies at the intersection of gathering data from sensors and extracting meaningful information from the sensor data. We envision that in the near future, majority of edge devices will be equipped with machine intelligence powered by deep learning. However, deep learning-based approaches require a large volume of high-quality data to train and are very expensive in terms of computation, memory, and power consumption. In this chapter, we describe eight research challenges and promising opportunities at the intersection of computer systems, networking, and machine learning. Solving those challenges will enable resource-limited edge devices to leverage the amazing capability of deep learning. We hope this chapter could inspire new research that will eventually lead to the realization of the vision of intelligent edge.