Fog Computing and Large Language Models: A vision for the mutual beneficiaries

2026-06-28

AI Summary

This work addresses the challenges of high latency, network overhead, and privacy concerns associated with deploying large language models (LLMs) in cloud-centric architectures for Internet of Things (IoT) applications. To overcome these limitations, the authors propose an integrated fog computing framework that brings LLMs closer to end devices through lightweighting techniques—including parameter quantization, pruning, and low-rank adaptation—enabling near-edge intelligence. The approach further incorporates resource-aware scheduling and LLM-driven automated code generation to facilitate dynamic deployment of fog applications. This study presents the first systematic architecture establishing a symbiotic relationship between fog computing and LLMs, demonstrating not only the feasibility of deploying LLMs in resource-constrained fog environments but also significantly enhancing fog nodes’ autonomous programming and service orchestration capabilities, thereby advancing the convergence of edge intelligence and generative AI.

Abstract

Fog computing utilizes proximal computational resources for sensor data processing and actuation, and addresses the latency, network load, and privacy issues of cloud-centric Internet of Things. On the other hand, Large Language Models (LLMs) are a type of deep learning AI models, which are trained on enormous text data, that perform various natural language processing tasks such as translation, question answering, text summarization, and code generation. LLMs are generally cloud-centric, requiring abundant GPU memory and computing capabilities, again face the same issues that led to fog computing. This pushes the necessity for LLM support in the proximity on fog infrastructure, requiring LLM optimizations such as parameter-weight quantization, pruning, low-rank adaptation etc. Meanwhile, fog computing also gets benefit from LLM's ability for code generation, in the dynamic deployment of fog-based applications. The paper addresses how both fog computing and LLMs can be mutual beneficiaries, discussing the state-of-the-art and future research scope.

Problem

• Fog Computing
• Large Language Models
• Edge AI
• Model Optimization
• Cloud-Edge Collaboration

Innovation

• Fog Computing
• Large Language Models
• Model Optimization
• Edge AI
• Code Generation