# Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AI

arXiv: https://arxiv.org/abs/2607.20937 · HTML: https://mlsanity.com/paper/2607.20937/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 103 references that are in the map.
- First posted on arXiv 23 Jul 2026.

## Builds on (most cited first, 23 of 23)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023)
- [Toward Edge General Intelligence with Agentic AI and Agentification: Concepts, Technologies, and Future Directions](https://mlsanity.com/paper/2508.18725/) (2025)
- [Edge AI: A Taxonomy, Systematic Review and Future Directions](https://mlsanity.com/paper/2407.04053/) (2024)
- [Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rows](https://mlsanity.com/paper/2107.00700/) (2021)
- [Automated Pest Detection with DNN on the Edge for Precision Agriculture](https://mlsanity.com/paper/2108.00421/) (2021)
- [Towards smart and adaptive agents for active sensing on edge devices](https://mlsanity.com/paper/2501.06262/) (2025)
- [Service Orchestration in the Computing Continuum: Structural Challenges and Vision](https://mlsanity.com/paper/2602.15794/) (2026)
- [LLM-based event abstraction and integration for IoT-sourced logs](https://mlsanity.com/paper/2409.03478/) (2024)
- [SLO-Aware Task Offloading within Collaborative Vehicle Platoons](https://mlsanity.com/paper/2409.17667/) (2024)
- [Abstractness, specificity, and complexity in software design](https://mlsanity.com/paper/1709.01304/) (2017)
- [Cyber Risk at the Edge: Current and future trends on Cyber Risk Analytics and Artificial Intelligence in the Industrial Internet of Things and Industry 4.0 Supply Chains](https://mlsanity.com/paper/1911.05726/) (2019)
- [Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing](https://mlsanity.com/paper/2203.07998/) (2022)
- [Semantic Data Sourcing for 6G Edge Intelligence](https://mlsanity.com/paper/2301.00403/) (2023)
- [Hiding task-oriented programming complexity: an industrial case study](https://mlsanity.com/paper/2303.02340/) (2023)
- [Achieving Observability on Fog Computing with the use of open-source tools](https://mlsanity.com/paper/2407.00035/) (2024)
- [Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devices](https://mlsanity.com/paper/2510.06882/) (2025)
- [Fog Computing and Large Language Models: A vision for mutual beneficiaries](https://mlsanity.com/paper/2606.29483/) (2026)

## Abstract (arXiv)

We are moving from an information age to the age of intelligence. A decade, or possibly less than that, data will not be the gold anymore rather the derived intelligence out of the data and the information we posses from the edge of the network. Existing Edge Intelligence research focuses mainly on two directions: using AI for edge resource management and deploying lightweight AI models on edge devices. However, existing edge computing research lacks an intelligence-centric framework in which derived intelligence is treated as a first-class, independently manageable entity that can be described, discovered, observed, shared, reused, and dynamically clustered across heterogeneous edge devices and applications. To address these research gaps, we introduced Clustered Edge Intelligence, a visionary intelligence-centric approach. The aim of CEI is to make intelligence a shareable and reusable first-class entity that can be independently represented, discovered, observed, exchanged, and managed across the distributed edge-cloud continuum. We present a three layer CEI architecture and examine enabling technologies and research dimensions, including intelligence inventories, semantic knowledge representation, communication, discoverability, observability, lifecycle automation, clustering mechanisms, marketplaces, interoperability, and standardization.