Deep Learning in the Era of Edge Computing: Challenges and Opportunities
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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.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Oct 2020.
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Citing papers per year: 2021: 4; 2022: 2; 2023: 1; 2024: 1; 2025: 1; 2026: 1.
10 papers.
- 2021Federated Learning for Internet of Things: Applications, Challenges, and Opportunitiescited by 50
- 2021DeepEdgeBench: Benchmarking Deep Neural Networks on Edge Devicescited by 26
- 2021When in Doubt, Summon the Titans: Efficient Inference with Large Modelscited by 8
- 2021FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspectivecited by 7
- 2022Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature reviewcited by 6
- 2023Towards Optimal Compression: Joint Pruning and Quantizationcited by 4
- 2022Federated Learning Hyper-Parameter Tuning from a System Perspectivecited by 1
- 2025From MNIST to ImageNet: Understanding the Scalability Boundaries of Differentiable Logic Gate Networkscited by 1
- 2024Knowledge Distillation Neural Network for Predicting Car-following Behaviour of Human-driven and Autonomous Vehiclescited by 0
- 2026E2CAR: An Efficient 2D-CNN Framework for Real-Time EEG Artifact Removal on Edge Devicescited by 0
- 2026E2CAR: An Efficient 2D-CNN Framework for Real-Time EEG Artifact Removal on Edge Devices30 Jan 2026
- 2025From MNIST to ImageNet: Understanding the Scalability Boundaries of Differentiable Logic Gate Networks30 Sep 2025
- 2024Knowledge Distillation Neural Network for Predicting Car-following Behaviour of Human-driven and Autonomous Vehicles8 Nov 2024
- 2023Towards Optimal Compression: Joint Pruning and Quantization15 Feb 2023
- 2022Federated Learning Hyper-Parameter Tuning from a System Perspective24 Nov 2022
- 2022Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review29 Apr 2022
- 2021Federated Learning for Internet of Things: Applications, Challenges, and Opportunities15 Nov 2021
- 2021When in Doubt, Summon the Titans: Efficient Inference with Large Models19 Oct 2021
- 2021FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective6 Oct 2021
- 2021DeepEdgeBench: Benchmarking Deep Neural Networks on Edge Devices21 Aug 2021