# Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks

arXiv: https://arxiv.org/abs/2402.06942 · HTML: https://mlsanity.com/paper/2402.06942/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2024.

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

- [Quality-of-Service Aware LLM Routing for Edge Computing with Multiple Experts](https://mlsanity.com/paper/2508.00234/) (2025)
- [Theory of Mixture-of-Experts for Mobile Edge Computing](https://mlsanity.com/paper/2412.15690/) (2024)
- [Mapping the Landscape of Generative AI in Network Monitoring and Management](https://mlsanity.com/paper/2502.08576/) (2025)
- [When MoE Meets Blockchain: A Trustworthy Distributed Framework of Large Models](https://mlsanity.com/paper/2509.12141/) (2025)
- [Double-Stage Feature-Level Clustering-Based Mixture of Experts Framework](https://mlsanity.com/paper/2503.09504/) (2025)
- [Mixture of Experts-augmented Deep Unfolding for Activity Detection in IRS-aided Systems](https://mlsanity.com/paper/2502.20183/) (2025)
- [A Model Aware AIGC Task Offloading Algorithm in IIoT Edge Computing](https://mlsanity.com/paper/2507.11560/) (2025)
- [Skillsets on the Chain: A Blockchain-based Zero-Trust Framework for Agentic AI Networking](https://mlsanity.com/paper/2608.00104/) (2026)

## Abstract (arXiv)

The advancement of generative artificial intelligence (GAI) has driven revolutionary applications like ChatGPT. The widespread of these applications relies on the mixture of experts (MoE), which contains multiple experts and selectively engages them for each task to lower operation costs while maintaining performance. Despite MoE, GAI faces challenges in resource consumption when deployed on user devices. This paper proposes mobile edge networks supported MoE-based GAI. We first review the MoE from traditional AI and GAI perspectives, including structure, principles, and applications. We then propose a framework that transfers subtasks to devices in mobile edge networks, aiding GAI model operation on user devices. We discuss challenges in this process and introduce a deep reinforcement learning based algorithm to select edge devices for subtask execution. Experimental results will show that our framework not only facilitates GAI's deployment on resource-limited devices but also generates higher-quality content compared to methods without edge network support.