mlsanityData to 14 Oct 2026

Towards Edge General Intelligence via Large Language Models: Opportunities and Challenges

arXiv 2410.18125 ↗First posted 16 Oct 2024cs.DC cs.AI cs.NIcited by 3

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Abstract (arXiv)

Edge Intelligence (EI) has been instrumental in delivering real-time, localized services by leveraging the computational capabilities of edge networks. The integration of Large Language Models (LLMs) empowers EI to evolve into the next stage: Edge General Intelligence (EGI), enabling more adaptive and versatile applications that require advanced understanding and reasoning capabilities. However, systematic exploration in this area remains insufficient. This survey delineates the distinctions between EGI and traditional EI, categorizing LLM-empowered EGI into three conceptual systems: centralized, hybrid, and decentralized. For each system, we detail the framework designs and review existing implementations. Furthermore, we evaluate the performance and throughput of various Small Language Models (SLMs) that are more suitable for development on edge devices. This survey provides researchers with a comprehensive vision of EGI, offering insights into its vast potential and establishing a foundation for future advancements in this rapidly evolving field.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 13 of its 17 references that are in the map. First posted on arXiv 16 Oct 2024.

Builds on

13 of 17 references.

References · 13

  1. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debatecited by 742
  2. 2024Hybrid LLM: Cost-Efficient and Quality-Aware Query Routingcited by 372
  3. 2023Building Cooperative Embodied Agents Modularly with Large Language Modelscited by 237
  4. 2023SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Modelscited by 153
  5. 2023Large Language Models Empowered Autonomous Edge AI for Connected Intelligencecited by 56
  6. 2023Large Language Model Enhanced Multi-Agent Systems for 6G Communicationscited by 53
  7. 2024EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and Votingcited by 27
  8. 2024AgentCoord: Visually Exploring Coordination Strategy for LLM-based Multi-Agent Collaborationcited by 26
  9. 2023Mobile Foundation Model as Firmwarecited by 25
  10. 2023LLMind: Orchestrating AI and IoT with LLM for Complex Task Executioncited by 24
  11. 2023EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edgecited by 21
  12. 2023ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Diseasecited by 14
1 more
  1. 2024An LLM-Based Digital Twin for Optimizing Human-in-the Loop Systemscited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Vision-Language Models for Edge Networks: A Comprehensive Surveycited by 29
  2. 2025Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challengescited by 3
  3. 2026Wasserstein Equilibrium Decoding for Reliable Medical Visual Question Answeringcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Resource-Aware LLM Reasoning for Mobile Edge General Intelligence
  2. 2025Fine-Tuning Small Language Models for Domain-Specific AI: An Edge AI Perspective
  3. 2026E2LLM: Towards Efficient LLM Serving in Heterogeneous Edge/Fog Environments
  4. 2025Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges
  5. 2024Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices
  6. 2023Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities
  7. 2025EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices
  8. 2025Large-Small Model Collaboration for Enhancing Edge-Deployed Small Models