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

Satish Narayana Srirama

arxiv: 2606.29483 · v1 · pith:RX6RKYLOnew · submitted 2026-06-28 · 💻 cs.DC

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

Satish Narayana Srirama This is my paper

Pith reviewed 2026-06-30 02:08 UTC · model grok-4.3

classification 💻 cs.DC

keywords fog computinglarge language modelsmodel optimizationIoTedge computingquantizationcode generation

0 comments

The pith

Fog computing and large language models can support each other through model optimizations and automated code generation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out a vision in which fog nodes supply the local compute needed to run large language models without constant cloud access, provided the models undergo size and speed reductions. In exchange, the language models supply code that lets fog systems deploy and adapt applications on the fly. This pairing targets the shared problems of latency, bandwidth use, and data exposure that affect both IoT sensor networks and current LLM services. A reader would care because it sketches a route toward AI and computing that stay closer to where data is produced.

Core claim

Fog computing and LLMs are mutual beneficiaries: fog infrastructure supports LLM deployment through optimizations such as parameter-weight quantization, pruning, and low-rank adaptation, while LLMs aid fog computing via code generation for dynamic application deployment.

What carries the argument

Quantization, pruning, and low-rank adaptation that shrink LLM memory and compute demands for fog hardware, paired with LLM code generation that automates fog application deployment.

If this is right

NLP tasks such as translation and summarization become feasible with lower latency on sensor networks.
Fog applications can be created and updated without manual coding for each deployment scenario.
Both systems reduce reliance on distant cloud data centers for routine operations.
Future research can explore combined fog-LLM stacks for privacy-sensitive IoT workloads.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Hybrid systems could shift routine LLM inference away from central clouds toward distributed nodes.
New performance metrics would be needed to judge how well compressed models serve fog-specific tasks.
The same code-generation approach might extend to other edge platforms beyond fog.

Load-bearing premise

The listed optimizations will let LLMs operate on resource-limited fog nodes without unacceptable drops in task performance or large increases in engineering effort.

What would settle it

A direct measurement showing that a quantized or pruned LLM running on typical fog hardware loses more than 15 percent accuracy on standard question-answering or summarization benchmarks compared with its cloud version.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Vision paper sketching mutual benefits between fog computing and LLMs but delivering no new results, data, or analysis.

read the letter

This paper is a short vision piece arguing that fog computing and LLMs can support each other. Fog nodes could run compressed LLMs, and LLMs could generate code to help deploy fog applications dynamically.

It organizes the two directions in one place and names the usual LLM optimizations—quantization, pruning, low-rank adaptation—as the route to edge deployment. It also notes the code-generation angle for fog apps and ties both ideas back to the classic fog motivations of latency and privacy.

The discussion stays high-level and cites the general state of the art without digging into specific studies or trade-offs. No experiments, measurements, or worked examples appear, so the practicality of the claimed synergies is left unexamined.

The soft spot is exactly that absence of evidence. The central assumption—that the listed optimizations will let LLMs run usefully on fog hardware without big accuracy or effort costs—is stated but not tested or even scoped. Readers already working in edge AI will recognize the points but gain little new insight.

The paper is for people in distributed systems or edge computing who want a quick map of possible intersections. It does not advance the technical state of the art.

I would not send it for full peer review. It could serve as a brief position statement in a workshop if the authors added concrete examples or references to existing deployments.

Referee Report

0 major / 2 minor

Summary. The manuscript presents a vision for mutual benefits between fog computing and large language models (LLMs). It argues that fog infrastructure can support LLM deployment on resource-constrained nodes via optimizations including parameter-weight quantization, pruning, and low-rank adaptation, while LLMs can assist fog computing through code generation for dynamic application deployment. The paper reviews relevant state-of-the-art work and outlines future research directions.

Significance. If the proposed conceptual synergies are pursued in follow-on work, the vision could help frame research at the intersection of edge computing and generative AI, particularly for latency-sensitive IoT applications. The paper's value is in its high-level synthesis rather than any new derivations, measurements, or validated predictions.

minor comments (2)

The abstract states that the paper discusses the state-of-the-art but does not indicate the specific sub-topics or organization of that discussion, which would improve readability for readers seeking targeted references.
The manuscript would benefit from explicit pointers to existing literature on LLM quantization or pruning applied to edge or fog-like hardware, even if only at a high level, to ground the vision more firmly.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their review and for recommending minor revision. The referee's summary correctly captures the paper as a high-level vision synthesizing synergies between fog computing and LLMs, with value in framing future research rather than presenting new empirical results.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The manuscript is a high-level vision paper outlining prospective synergies between fog computing and LLMs, with no equations, derivations, fitted parameters, predictions, or load-bearing self-citations. It discusses optimizations such as quantization and pruning at a conceptual level and lists future research directions without any reduction of claims to inputs by construction or via self-referential definitions. The central claims remain independent of any internal circular structure.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Vision paper with no mathematical model, fitted parameters, or new postulated entities; all content rests on standard background assumptions about fog and LLM capabilities.

pith-pipeline@v0.9.1-grok · 5688 in / 973 out tokens · 25916 ms · 2026-06-30T02:08:32.135465+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

19 extracted references · 4 canonical work pages · 1 internal anchor

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S. N. Srirama, “A decade of research in fog com- puting: Relevance, challenges, and future directions,” Software: Practice and Experience, vol. 54, no. 1, pp. 3–23, 2024

2024
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Adapted large language models can outperform medical experts in clinical text summarization,

D. Van Veen, C. Van Uden, L. Blankemeier, J.-B. Del- brouck, A. Aali, C. Bluethgen, A. Pareek, M. Polacin, E. P . Reis, A. Seehofnerováet al., “Adapted large language models can outperform medical experts in clinical text summarization,”Nature medicine, vol. 30, no. 4, pp. 1134–1142, 2024

2024
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work page arXiv 2026
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M.-L. Wang, R.-P . Zhang, W.-J. Wu, Y . Lu, X.-E. Wei, Z. Sun, B.-H. Guan, J.-J. Zhang, X. Wu, L. Zhang et al., “Evaluation of large language models for diag- nostic impression generation from brain MRI report findings: A multicenter benchmark and reader study,” npj Digital Medicine, 2026

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work page arXiv 2024
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2026
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S. Basak and S. N. Srirama, “Fog computing out of the box: Dynamic deployment of fog service contain- ers with TOSCA,”International Journal of Network Management, vol. 34, no. 5, p. e2246, 2024

2024
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2024
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IaC Generation with LLMs: An Error Taxonomy and A Study on Configuration Knowledge Injection

R. Nekrasov, S. Fossati, I. Kumara, D. A. Tamburri, and W.-J. v. d. Heuvel, “IaC generation with LLMs: An error taxonomy and a study on configuration knowl- edge injection,”arXiv preprint arXiv:2512.14792, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025
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P . K. Donta, A. Saleh, Y . Li, S. Vaishnav, K. Fang, H. Feng, Y . Xia, T. R. Gadekallu, Q. Zhang, X. Shi et al., “Socio-technical aspects of Agentic AI,”arXiv preprint arXiv:2601.06064, 2025. Satish Narayana Sriramais a Professor at the School of Computer and Information Sciences, University of Hyderabad, India. He is also a Visiting Professor and the ho...

work page arXiv 2025
[19]

He is an IEEE Senior 8 Publication Title Feb 2026 Fog Computing and LLM Conglomeration Member, and an Editor of Wiley Software: Practice and Experience, a 56-year-old Journal

His current research focuses on cloud comput- ing, mobile web services, mobile cloud, Internet of Things, fog computing, migrating scientific computing and enterprise applications to the cloud, and large- scale data analytics on the cloud. He is an IEEE Senior 8 Publication Title Feb 2026 Fog Computing and LLM Conglomeration Member, and an Editor of Wiley...

2026

[1] [1]

A decade of research in fog com- puting: Relevance, challenges, and future directions,

S. N. Srirama, “A decade of research in fog com- puting: Relevance, challenges, and future directions,” Software: Practice and Experience, vol. 54, no. 1, pp. 3–23, 2024

2024

[2] [2]

Attention is all you need,

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,”Advances in neural infor- mation processing systems, vol. 30, 2017

2017

[3] [3]

A review on LLMs for IoT ecosystem: State-of-the-art, lightweight models, use cases, key challenges, future directions,

P . P . Ray, “A review on LLMs for IoT ecosystem: State-of-the-art, lightweight models, use cases, key challenges, future directions,”Internet of Things and Cyber-Physical Systems, vol. 5, pp. 275–328,

[4] [4]

Available: https://www.sciencedirect

[Online]. Available: https://www.sciencedirect. com/science/article/pii/S2667345226000088

[5] [5]

Adapted large language models can outperform medical experts in clinical text summarization,

D. Van Veen, C. Van Uden, L. Blankemeier, J.-B. Del- brouck, A. Aali, C. Bluethgen, A. Pareek, M. Polacin, E. P . Reis, A. Seehofnerováet al., “Adapted large language models can outperform medical experts in clinical text summarization,”Nature medicine, vol. 30, no. 4, pp. 1134–1142, 2024

2024

[6] [6]

Benchmarking LLM summaries of mul- timodal clinical time series for remote monitoring,

A. Shukla, Y . Yuan, B. Tamo, Y . Wang, M. Nnamdi, S. Tan, J. Li, B. Marteau, B. Willingham, and M. Wang, “Benchmarking LLM summaries of mul- timodal clinical time series for remote monitoring,” arXiv preprint arXiv:2603.01557, 2026

work page arXiv 2026

[7] [7]

Evaluation of large language models for diag- nostic impression generation from brain MRI report findings: A multicenter benchmark and reader study,

M.-L. Wang, R.-P . Zhang, W.-J. Wu, Y . Lu, X.-E. Wei, Z. Sun, B.-H. Guan, J.-J. Zhang, X. Wu, L. Zhang et al., “Evaluation of large language models for diag- nostic impression generation from brain MRI report findings: A multicenter benchmark and reader study,” npj Digital Medicine, 2026

2026

[8] [8]

On-device language models: A comprehensive review, 2024

J. Xu, Z. Li, W. Chen, Q. Wang, X. Gao, Q. Cai, and Z. Ling, “On-device language models: A comprehen- sive review,”arXiv preprint arXiv:2409.00088, 2024

work page arXiv 2024

[9] [9]

What is a multimodal LLM (MLLM)?

J. Varughese, “What is a multimodal LLM (MLLM)?” https://www.ibm.com/think/topics/multimodal-llm, ac- cessed: May 23, 2026

2026

[10] [10]

Hubert: Self-supervised speech representation learning by masked prediction of hidden units,

W.-N. Hsu, B. Bolte, Y .-H. H. Tsai, K. Lakho- tia, R. Salakhutdinov, and A. Mohamed, “Hubert: Self-supervised speech representation learning by masked prediction of hidden units,”IEEE/ACM trans- actions on audio, speech, and language processing, vol. 29, pp. 3451–3460, 2021

2021

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TOSCA version 2.0, OASIS standard,

C. Lauwers and C. Curescu, “TOSCA version 2.0, OASIS standard,” https://docs.oasis-open.org/tosca/ TOSCA/v2.0/TOSCA-v2.0.html, Jul. 2025

2025

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Fog computing out of the box: Dynamic deployment of fog service contain- ers with TOSCA,

S. Basak and S. N. Srirama, “Fog computing out of the box: Dynamic deployment of fog service contain- ers with TOSCA,”International Journal of Network Management, vol. 34, no. 5, p. e2246, 2024

2024

[13] [13]

A survey on llm- based code generation for low-resource and domain- specific programming languages,

S. Joel, J. Wu, and F . Fard, “A survey on llm- based code generation for low-resource and domain- specific programming languages,”ACM Transactions on Software Engineering and Methodology, 2024

2024

[14] [14]

RADON particles repository,

“RADON particles repository,” https://github.com/ radon-h2020/radon-particles, accessed: May 23, 2026

2026

[15] [15]

Radon: ra- tional decomposition and orchestration for serverless computing,

G. Casale, M. Arta ˇc, W.-J. Van Den Heuvel, A. van Hoorn, P . Jakovits, F . Leymann, M. Long, V. Pa- panikolaou, D. Presenza, A. Russoet al., “Radon: ra- tional decomposition and orchestration for serverless computing,”SICS Software-Intensive Cyber-Physical Systems, vol. 35, no. 1, pp. 77–87, 2020

2020

[16] [16]

Iac-eval: A code generation benchmark for cloud infrastructure-as-code programs,

P . T. Kon, J. Liu, Y . Qiu, W. Fan, T. He, L. Lin, H. Zhang, O. M. Park, G. S. Elengikal, Y . Kang et al., “Iac-eval: A code generation benchmark for cloud infrastructure-as-code programs,”Advances in Neural Information Processing Systems, vol. 37, pp. 134 488–134 506, 2024

2024

[17] [17]

IaC Generation with LLMs: An Error Taxonomy and A Study on Configuration Knowledge Injection

R. Nekrasov, S. Fossati, I. Kumara, D. A. Tamburri, and W.-J. v. d. Heuvel, “IaC generation with LLMs: An error taxonomy and a study on configuration knowl- edge injection,”arXiv preprint arXiv:2512.14792, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025

[18] [18]

Socio-technical aspects of Agentic AI,

P . K. Donta, A. Saleh, Y . Li, S. Vaishnav, K. Fang, H. Feng, Y . Xia, T. R. Gadekallu, Q. Zhang, X. Shi et al., “Socio-technical aspects of Agentic AI,”arXiv preprint arXiv:2601.06064, 2025. Satish Narayana Sriramais a Professor at the School of Computer and Information Sciences, University of Hyderabad, India. He is also a Visiting Professor and the ho...

work page arXiv 2025

[19] [19]

He is an IEEE Senior 8 Publication Title Feb 2026 Fog Computing and LLM Conglomeration Member, and an Editor of Wiley Software: Practice and Experience, a 56-year-old Journal

His current research focuses on cloud comput- ing, mobile web services, mobile cloud, Internet of Things, fog computing, migrating scientific computing and enterprise applications to the cloud, and large- scale data analytics on the cloud. He is an IEEE Senior 8 Publication Title Feb 2026 Fog Computing and LLM Conglomeration Member, and an Editor of Wiley...

2026