# OpenEI: An Open Framework for Edge Intelligence

arXiv: https://arxiv.org/abs/1906.01864 · HTML: https://mlsanity.com/paper/1906.01864/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 12 of 12)

- [Mobile Edge Intelligence for Large Language Models: A Contemporary Survey](https://mlsanity.com/paper/2407.18921/) (2024)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [Efficient Federated Meta-Learning over Multi-Access Wireless Networks](https://mlsanity.com/paper/2108.06453/) (2021)
- [Efficient Softmax Approximation for Deep Neural Networks with Attention Mechanism](https://mlsanity.com/paper/2111.10770/) (2021)
- [Enabling All In-Edge Deep Learning: A Literature Review](https://mlsanity.com/paper/2204.03326/) (2022)
- [Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems](https://mlsanity.com/paper/2207.14694/) (2022)
- [FeSHI: Feature Map Based Stealthy Hardware Intrinsic Attack](https://mlsanity.com/paper/2106.06895/) (2021)
- [An Edge Computing-Based Solution for Real-Time Leaf Disease Classification using Thermal Imaging](https://mlsanity.com/paper/2411.03835/) (2024)
- [Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning](https://mlsanity.com/paper/2101.00687/) (2021)
- [SAFEMYRIDES: Application of Decentralized Control Edge-Computing to Ridesharing Monitoring Services](https://mlsanity.com/paper/2301.00888/) (2023)
- [An Agentic Framework for Rapid Deployment of Edge AI Solutions in Industry 5.0](https://mlsanity.com/paper/2510.25813/) (2025)
- [Delta Sum Learning: an approach for fast and global convergence in Gossip Learning](https://mlsanity.com/paper/2512.01549/) (2025)

## Abstract (arXiv)

In the last five years, edge computing has attracted tremendous attention from industry and academia due to its promise to reduce latency, save bandwidth, improve availability, and protect data privacy to keep data secure. At the same time, we have witnessed the proliferation of AI algorithms and models which accelerate the successful deployment of intelligence mainly in cloud services. These two trends, combined together, have created a new horizon: Edge Intelligence (EI). The development of EI requires much attention from both the computer systems research community and the AI community to meet these demands. However, existing computing techniques used in the cloud are not applicable to edge computing directly due to the diversity of computing sources and the distribution of data sources. We envision that there missing a framework that can be rapidly deployed on edge and enable edge AI capabilities. To address this challenge, in this paper we first present the definition and a systematic review of EI. Then, we introduce an Open Framework for Edge Intelligence (OpenEI), which is a lightweight software platform to equip edges with intelligent processing and data sharing capability. We analyze four fundamental EI techniques which are used to build OpenEI and identify several open problems based on potential research directions. Finally, four typical application scenarios enabled by OpenEI are presented.