# Toward Democratized Generative AI in Next-Generation Mobile Edge Networks

arXiv: https://arxiv.org/abs/2411.09148 · HTML: https://mlsanity.com/paper/2411.09148/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2024.

## Built on by (most cited first, 8 of 8)

- [Diffusion Models for Future Networks and Communications: A Comprehensive Survey](https://mlsanity.com/paper/2508.01586/) (2025)
- [When Graph Meets Retrieval Augmented Generation for Wireless Networks: A Tutorial and Case Study](https://mlsanity.com/paper/2412.07189/) (2024)
- [Skill Description Deception Attack against Task Routing in Internet of Agents](https://mlsanity.com/paper/2605.09889/) (2026)
- [BERT4beam: Large AI Model Enabled Generalized Beamforming Optimization](https://mlsanity.com/paper/2509.11056/) (2025)
- [Generative AI Meets Wireless Sensing: Towards Wireless Foundation Model](https://mlsanity.com/paper/2509.15258/) (2025)
- [Adaptive Pruning for Large Language Models with Structural Importance Awareness](https://mlsanity.com/paper/2412.15127/) (2024)
- [Safe and Economical UAV Trajectory Planning in Low-Altitude Airspace: A Hybrid DRL-LLM Approach with Compliance Awareness](https://mlsanity.com/paper/2506.08532/) (2025)
- [Resource-Aware LLM Reasoning for Mobile Edge General Intelligence](https://mlsanity.com/paper/2509.23248/) (2025)

## Abstract (arXiv)

The rapid development of generative AI technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments.