LLMs meet Federated Learning for Scalable and Secure IoT Management

Amiya Nayak; Arghavan Asad; Yazan Otoum

REVIEW 4 major objections 7 minor 1 cited by

LLMs meet Federated Learning for Scalable and Secure IoT Management

T0 review · 4 major / 7 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read The paper claims that a gradient-sensing federated strategy (GSFS) lets edge devices fine-tune LLMs for IoT tasks with less communication and lower latency than standard FedAvg or FedOpt, while keeping accuracy and privacy.

desk verdict A modest engineering tweak whose headline efficiency claim is undercut by a throughput-based 'energy' metric and inconsistent latency numbers across tables. read the letter →

arxiv 2504.16032 v2 pith:EB7QTCNO submitted 2025-04-22 cs.LG cs.AIcs.ET

Yazan Otoum , Arghavan Asad , Amiya Nayak This is my paper · ORCID

classification cs.LGcs.AIcs.ET

keywords federatedlearninglargelanguagemodelsInternetofThingsGradientSensingStrategyedge-cloudcomputingIoT-23datasetcommunicationefficiencyprivacy-preservingtraining

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper aims to show that large language models can be fine-tuned for IoT management in a federated, privacy-preserving way without the communication and latency costs of standard federated averaging. It introduces Gradient Sensing Federated Strategy (GSFS), a client-server update rule in which edge devices upload model updates only when their local validation metric or gradient norm crosses an adaptive threshold, and the cloud aggregates only when a pool of distinct client updates reaches a threshold. On the cleaned IoT-23 dataset, the paper reports that GSFS outperforms FedAvg and FedOpt in accuracy and F1, cuts response latency, and raises throughput. A sympathetic reader would care because the mechanism addresses the main obstacle to using LLMs at the edge: high communication and compute cost.

What carries the argument

The core mechanism is GSFS, a gradient-sensing update policy. Each client evaluates its local model on a private validation set and tracks layer-wise gradient norms, maintaining a moving mean and standard deviation. If the change in the performance metric exceeds an adaptive threshold, or if any layer's gradient norm exceeds its adaptive threshold, the client asynchronously uploads its update to the server. The server caches these updates in a pool and performs global aggregation only when the pool contains updates from a threshold fraction (default 60%) of clients, weighting updates by submission frequency. The aggregated global model is then broadcast to all clients, which reset their local reference points. This selective, asynchronous upload design is what the paper claims reduces communication overhead and latency relative to uniform full-participation rounds.

What would settle it

Measure actual wall-clock power draw (in watts) of the central server and client GPUs across the 10 federated rounds for GSFS, FedAvg, and FedOpt, and compare energy consumed per completed task; if GSFS does not consume less energy per task, the energy-efficiency claim fails. Also re-run Table III with latency in consistent units (milliseconds) and verify whether the central latency of 37.74 seconds is test-set inference time or includes aggregation and communication overhead.

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Extended reading notes

Core claim

The central discovery is that a federated strategy which lets clients upload model updates only when their local performance or gradient norms shift significantly—and lets the server aggregate only once enough distinct clients have reported—can match or exceed the accuracy of round-synchronous FedAvg and FedOpt while lowering response latency and raising throughput, at least on the cleaned IoT-23 dataset with OPT-family LLMs. Specifically, the paper reports central accuracy of 0.9008 against 0.8945 for FedAvg, client-side latency reduced from 71.43 s to 34.69 s, and higher throughput in requests per minute.

Load-bearing premise

The reported energy and latency advantages depend on treating 'requests per minute' as a measure of energy efficiency and on comparing latency numbers collected under the same units and conditions in Tables II and III; if either is not the case, the efficiency claims collapse.

Editorial extensions

If this is right

Federated fine-tuning of LLMs for IoT becomes feasible on devices with limited memory and GPU capacity, since clients upload only significant updates rather than every round.
Communication volume per round drops because not every client participates every round; the server aggregates only when enough distinct clients report.
Applications such as malware detection and anomaly detection on IoT network traffic can be updated continuously from edge data without raw data leaving the device.
The hybrid edge-cloud deployment lets small client models run on commodity GPUs while the cloud maintains a larger central model for global refinement.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

An extension not explored here: the same selective-upload trigger could be stress-tested on non-IID client data, where infrequent uploads may delay the server's view of local distribution shifts and potentially hurt convergence.
If the paper's 'energy efficiency' proxy is replaced with actual measured joules per task, the ranking among strategies could change; a direct power measurement is a natural test of the claimed efficiency gain.
The GSFS aggregation could be extended with staleness-aware weights, so older updates receive lower weight than fresh ones; the paper currently weights only by submission frequency.
The threshold parameters (smoothing factor and sensitivity) likely interact with client heterogeneity, so a sensitivity analysis over these hyperparameters would clarify when GSFS helps rather than hurts.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: the GSFS scheme is a reasonable engineering combination of existing asynchronous federated learning ideas—performance-triggered uploads, gradient-norm thresholds, and server-side weighted aggregation—applied to LLM fine-tuning for IoT. The writing is clear and the IoT-23 comparison across several LLMs is useful. But the paper's central efficiency claim rests on a metric labeled 'energy efficiency' that is actually throughput (requests/minute), and the latency numbers in Table II and Table III don't reconcile. That's a load-bearing flaw, and the accuracy gain is tiny anyway.

What's good: the architecture description is concrete, the selection of OPT-350M as client model is justified by Table II, and the equations for GSFS are complete enough to reimplement. The idea of letting clients decide when to upload based on local change is sensible and can reduce communication. If the only claim were 'a threshold-based asynchronous FL variant that keeps accuracy while lowering communication', the paper would be a modest but acceptable contribution.

Where it falls down: the abstract reports 1.02% client accuracy improvement, but Table III shows 1.49 percentage points (0.8732 vs 0.8881). That's an internal inconsistency. More seriously, 'Energy Efficiency' is defined as requests per minute, which is throughput. Under that definition, any strategy that cuts communication can mechanically raise the number of requests handled, so the claimed energy improvement is partly baked into the metric. There is no measured energy. The latency problem: Table II reports OPT-350M at 88.14 ms and 680.70 req/min. Table III reports the GSFS client model at 34.69 s and 1.73 req/min. If these are the same model on the same client hardware, that's about a 390x slowdown, which makes no sense unless the quantities measure different things. The paper never reconciles these. There's also no code, no confidence intervals, and no comparison to existing asynchronous FL methods like FedAsync, so the novelty isn't positioned against the work that actually matters.

Who it's for: someone wanting a quick survey of FL-LLM for IoT might skim it, but anyone considering GSFS needs the efficiency numbers fixed first. The core algorithm is plausible, but the evaluation doesn't establish the headline result.

Recommendation: this deserves peer review, not a desk reject—the flaws are fixable and the engineering idea has some merit. A careful referee should ask for a real energy measurement (or a clear argument that throughput is a valid proxy), a reconciliation of the latency tables, corrected accuracy deltas, and a comparison to asynchronous baselines. If those come back, the paper could be salvageable.

Referee Report

4 major / 7 minor

Summary. The paper proposes GSFS, an asynchronous federated strategy for fine-tuning LLMs in IoT environments. Clients upload updates only when local performance or gradient thresholds are exceeded, and the server aggregates when a threshold number of distinct clients has contributed. The framework is evaluated on the IoT-23 dataset using OPT models, comparing against FedAvg and FedOpt. The paper claims that GSFS improves model accuracy, reduces response latency, and enhances energy efficiency.

Significance. The motivating problem is relevant: fine-tuning LLMs under federated IoT constraints involves communication and resource bottlenecks, and a threshold-triggered asynchronous update strategy with server-side aggregation is a plausible mechanism for reducing communication. However, the empirical support is undermined by the metric definitions and internal inconsistencies described below. The paper does not provide code, configuration values, or a data availability statement, so the experiments are not reproducible. If the efficiency claims were supported by direct energy measurements and the latency units were reconciled, the contribution could be useful to the FL-for-IoT community.

major comments (4)

[Section IV-A, Table III] The metric 'Energy Efficiency' is defined as 'the number of requests the model can handle within one minute' (req/min), which is throughput, not energy. No measurement of power or energy per request is provided anywhere in the paper. Consequently, the abstract's claim of 'enhanced energy efficiency' and Section IV-B's statement that 'GSFS achieves higher efficiency than FedAvg' are not supported. Because GSFS is designed to reduce communication and synchronization overhead, it can mechanically increase throughput by freeing resources, making the claimed improvement partly constructed by the metric definition. The authors should either measure actual energy consumption (e.g., Joules per request) or relabel the metric as throughput and remove the energy-efficiency claim.
[Section IV-A, Tables II and III] The latency and throughput values in Table II and Table III are not commensurable. Table II reports OPT-350M client latency as 88.14 ms and throughput as 680.70 req/min, while Table III reports the GSFS client model latency as 34.69 s and throughput as 1.73 req/min. These numbers imply a roughly 394x slowdown and 393x lower throughput if they refer to the same quantity. The paper must state the exact unit and aggregation procedure for the latency values in Table III and reconcile the throughput numbers with Table II; without this, the 'lower response latency' claim for GSFS cannot be verified.
[Abstract and Section IV-B] The abstract states that GSFS achieves '1.02% higher accuracy on the client model' compared to FedAvg, but Table III reports FedAvg client accuracy as 0.8732 and GSFS client accuracy as 0.8881, which is a 1.49 percentage-point gain (1.71% relative), not 1.02%. The reported number is internally inconsistent with the results table and should be corrected, with all derived claims rechecked against the table values.
[Title/Abstract vs Section IV] The title and abstract describe the framework as 'Secure IoT Management' and mention Differential Privacy and TLS/SSL, but no privacy or security evaluation is presented. The experiments measure accuracy, F1-score, latency, and throughput only. The security claims are load-bearing for the paper's framing, so the authors should either add a privacy/security evaluation (e.g., privacy budget, threat model, or attack resistance) or substantially qualify the security claims.

minor comments (7)

[Index Terms] The index term 'Large Learning Models' should be 'Large Language Models' for consistency with the abstract and body.
[Section III-A] The abstract introduces 'Generative IoT (GIoT) models,' but the framework description in Section III never defines or uses this term; please clarify or remove it.
[Section IV-B] The threshold parameters δ_perf, β, α, and M from Eqs. (8), (11), and the aggregation rule are not given concrete values or a sensitivity analysis, so the reported results are not reproducible from the text.
[Figure 2] The caption for Figure 2 does not state which training configuration or FL strategy produced the loss curves, and the axis labels are not visible in the manuscript text; please provide this information.
[Section III-B] The paper mentions 'Differential Privacy (DP-FedAvg)' as a secure aggregation technique but provides no DP mechanism, privacy budget, or evaluation, so this claim is currently unsupported.
[References] The related work section cites only a few FL-LLM papers and omits the substantial literature on asynchronous and communication-efficient federated learning (e.g., FedAsync, FedProx), which would help position the contribution more accurately.
[General] The paper does not include a data or code availability statement, which limits reproducibility and is expected for empirical work in this area.

Circularity Check

1 steps flagged · score 4.0 of 10

Energy-efficiency improvement is the reciprocal of the response-latency improvement by definition; no load-bearing self-citation or fitted prediction found.

self definitional [Section IV-A (Metric Definitions); Table III]
"Response Latency: The average inference time required for the model to process the test set. Energy Efficiency: The number of requests the model can handle within one minute."

The paper defines response latency as seconds per request and energy efficiency as requests per minute. Under these definitions, requests per minute is exactly 60 divided by the latency in seconds, so the two metrics are the same quantity expressed reciprocally. Table III confirms the identity for every row: GSFS client latency 34.69 s gives 60/34.69 = 1.73 req/min, FedAvg client latency 71.43 s gives 60/71.43 = 0.84, and so on. Consequently, the claimed 'energy efficiency' improvement is not an independent finding; it is a deterministic transform of the claimed response-latency improvement. The abstract's separate promises of 'reduces response latency' and 'enhances energy efficiency' therefore reduce to a single latency measurement, not two independent results.

full rationale

The central novelty, GSFS, is an asynchronous, threshold-triggered federated update strategy. The paper does not derive GSFS from a first-principles argument, and it does not invoke a uniqueness theorem or a load-bearing self-citation: the two self-citations ([1], [4]) appear only in background sentences about cloud-centric scalability and healthcare-sensitive domains, not in the mechanism or evaluation. The accuracy improvements are empirical values from Table III and are not fitted parameters renamed as predictions. The main circularity is a definitional double-count: 'energy efficiency' is defined as requests per minute, which is exactly 60 divided by the response latency in seconds, so the claimed energy-efficiency advantage is just the reciprocal of the claimed latency advantage. This affects the paper's differentiator, but the latency and accuracy measurements still have independent empirical content, and the numerical inconsistency in the abstract's client-accuracy percentage (1.02% claimed vs. 1.49 percentage points from Table III) is a reporting error rather than a circular derivation. Overall, the efficiency claim is partly constructed by metric definition, but the core federated-learning comparison is not circular.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The framework introduces no new physical or conceptual entities. All free parameters are algorithm hyperparameters that are not reported in the experiments, making the central empirical results difficult to interpret or reproduce. The assumptions above are load-bearing premises for the validity of the reported metrics and the relevance to real IoT systems.

free parameters (4)

Performance trigger threshold δ_perf
Defined in Eq. (8) but its value in the experiments is not reported. The upload frequency and thus communication behavior depends on it.
Gradient norm sensitivity β
Defined in Eq. (11) as the multiplier on the standard deviation. Not specified in the experimental section.
Smoothing factor α
Defined in Eq. (9)-(10) for the moving average of gradient norms. Not specified in the experiments.
Server aggregation threshold M = 60% of clients (default stated in text)
The paper says M defaults to 60% of clients, but the actual number of clients is not reported, so the effective threshold is unknown.

assumptions (4)

domain assumption The IoT-23 dataset classification task is a representative proxy for IoT management and security performance.
The paper uses a single cleaned dataset and generalizes to IoT management, but no evidence connects this classification task to real IoT deployment metrics.
domain assumption Requests per minute is a valid measure of energy efficiency.
The paper explicitly defines Energy Efficiency as the number of requests the model can handle within one minute (Section IV-A), but this is throughput, not energy. The conclusion of improved energy efficiency relies on this assumption.
domain assumption An RTX 3080 Ti GPU with 12 GB VRAM is representative of an edge or IoT client device.
The client environment is described as resource-limited, but 12 GB VRAM is far more powerful than typical IoT hardware. The claimed client-side latency and efficiency results may not transfer to real IoT devices.
domain assumption FedAvg and FedOpt baselines are implemented with comparably tuned hyperparameters and the same client/data partition as GSFS.
The experimental setup does not describe how baselines were configured, how many clients participated, or how data was distributed. Without this, the comparison may be unfair to the baselines.

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Cite this review

Pith. "Pith review of LLMs meet Federated Learning for Scalable and Secure IoT Management." pith.science (2026). https://pith.science/paper/EB7QTCNO

@misc{pith2026250416032,
  author       = {Pith},
  title        = {Pith review of: LLMs meet Federated Learning for Scalable and Secure IoT Management},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EB7QTCNO}},
  note         = {Machine review of arXiv:2504.16032}
}

read the original abstract

The rapid expansion of IoT ecosystems introduces severe challenges in scalability, security, and real-time decision-making. Traditional centralized architectures struggle with latency, privacy concerns, and excessive resource consumption, making them unsuitable for modern large-scale IoT deployments. This paper presents a novel Federated Learning-driven Large Language Model (FL-LLM) framework, designed to enhance IoT system intelligence while ensuring data privacy and computational efficiency. The framework integrates Generative IoT (GIoT) models with a Gradient Sensing Federated Strategy (GSFS), dynamically optimizing model updates based on real-time network conditions. By leveraging a hybrid edge-cloud processing architecture, our approach balances intelligence, scalability, and security in distributed IoT environments. Evaluations on the IoT-23 dataset demonstrate that our framework improves model accuracy, reduces response latency, and enhances energy efficiency, outperforming traditional FL techniques (i.e., FedAvg, FedOpt). These findings highlight the potential of integrating LLM-powered federated learning into large-scale IoT ecosystems, paving the way for more secure, scalable, and adaptive IoT management solutions.

Figures

Figures reproduced from arXiv: 2504.16032 by the authors.

**Figure 1.** Architecture of our proposed IoT management system integrating edge and cloud computing with federated learning. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** Loss curves for the OPT-350M and OPT-1.3b models [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems
cs.CR 2025-05 reject novelty 3.0 of 10

Fine-tuned BERT-small reaches 99.75% test accuracy on combined IoT-23 and TON IoT attack classification, but the claimed real-time prevention advantage over traditional methods is not demonstrated.

Reference graph

Works this paper leans on

11 extracted references · 8 canonical work pages · cited by 1 Pith paper

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

E. H. Said, Y. Otoum, and A. Nayak, ``A scalable meta learning-based model to secure iot networks,'' IEEE Internet of Things Magazine, vol. 6, no. 2, pp. 116--120, 2023

work page 2023

[2] [2]

Nazari Jahantigh, A

M. Nazari Jahantigh, A. Masoud Rahmani, N. Jafari Navimirour, and A. Rezaee, ``Integration of internet of things and cloud computing: a systematic survey,'' IET Communications, vol. 14, no. 2, pp. 165--176, 2020

work page 2020

[3] [3]

T. Vo, P. Dave, G. Bajpai, and R. Kashef, ``Edge, fog, and cloud computing: An overview on challenges and applications,'' arXiv preprint arXiv:2211.01863, 2022

work page Pith review arXiv 2022

[4] [4]

Otoum, P

Y. Otoum, P. Singh, and A. Nayak, ``Advancing iomt defenses: Deep collaborative learning for robust healthcare security,'' in GLOBECOM 2024-2024 IEEE Global Communications Conference. 1em plus 0.5em minus 0.4em IEEE, 2024, pp. 2966--2971

work page 2024

[5] [5]

M. Zong, A. Hekmati, M. Guastalla, Y. Li, and B. Krishnamachari, ``Integrating large language models with internet of things: applications,'' Discover Internet of Things, vol. 5, no. 1, p. 2, 2025

work page 2025

[6] [6]

N. P. Tran, B. Jaumard, and O. Delgado, ``Energy-aware llms: A step towards sustainable ai for downstream applications,'' arXiv preprint arXiv:2503.17783, 2025

work page Pith review arXiv 2025

[7] [7]

Kairouz, H

P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings et al., ``Advances and open problems in federated learning,'' Foundations and trends in machine learning , vol. 14, no. 1--2, pp. 1--210, 2021

work page 2021

[8] [8]

T. An, Y. Zhou, H. Zou, and J. Yang, ``Iot-llm: Enhancing real-world iot task reasoning with large language models,'' arXiv preprint arXiv:2410.02429, 2024

arXiv 2024