A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environments

Gaith Rjouba; Hanae Elmekki; Jamal Bentahar; Rachida Dssouli; Saidul Islam

REVIEW 4 major objections 6 minor 13 references

A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environments

T0 review · 4 major / 6 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read The paper tries to establish that a hybrid of PSO and ACO can lift federated MLLM deployment to 92% accuracy while cutting communication cost by 30%.

desk verdict Reasonable hybrid idea, but the missing FL baseline and unreported experimental protocol sink the current claims. read the letter →

arxiv 2502.10419 v1 pith:EXZ7NAMW submitted 2025-02-04 cs.NE cs.AIcs.LG

Gaith Rjouba , Hanae Elmekki , Saidul Islam , Jamal Bentahar , Rachida Dssouli This is my paper · ORCID

classification cs.NEcs.AIcs.LG

keywords FederatedLearningMultimodalLargeLanguageModelsSwarmIntelligenceParticleOptimizationAntColonyEdge-CloudComputingResourceNon-IIDData

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 tries to establish that a hybrid of two swarm-intelligence algorithms can solve the two bottlenecks that keep multimodal large language models (MLLMs) out of federated edge-cloud training: deciding which resource-limited edge devices should fine-tune the model, and finding low-cost transmission paths for the updates. Particle Swarm Optimization (PSO) picks the device subset from energy, data relevance, and data diversity; Ant Colony Optimization (ACO) routes the model updates back to the cloud. If correct, federated MLLM fine-tuning could run on private, heterogeneous edge data without exhausting device batteries or swamping the network. The authors report 92% accuracy on an unmanned-vehicle decision task, about a 30% communication-cost reduction, higher client participation, and faster convergence than rule-based, CNN-LSTM, and deep-reinforcement-learning baselines.

What carries the argument

The framework's load-bearing machinery is the pair of optimization loops that decide who trains and how updates travel. Particle Swarm Optimization (PSO) is a swarm search in which candidate solutions (particles) move toward their own best-known position and the swarm's best position; Ant Colony Optimization (ACO) is a path search in which ants reinforce good routes with pheromone. PSO scores each candidate device subset $S$ by a fitness function $Fitness(S)=\alpha\sum_{i\in S} E_i+\beta\sum_{i\in S}(1-R_i)+\gamma\sum_{i\in S} D_i$ (Eq. 16), where $E_i$ is energy consumption, $R_i$ is data relevance, and $D_i$ is data diversity, with weights $\alpha,\beta,\gamma$; particles update velocities and positions by the standard PSO equations (Eqs. 17-18). ACO chooses communication links with probability $P_{ij}=\tau_{ij}^{\alpha}\eta_{ij}^{\beta}/\sum_{k\in neighbors} \tau_{ik}^{\alpha}\eta_{ik}^{\beta}$ (Eq. 19), where $\tau_{ij}$ is pheromone and $\eta_{ij}$ is a heuristic favoring short or high-bandwidth links, and then evaporates and deposits pheromone (Eq. 20). The selected devices' updates are combined in the cloud as $w_{global}=\sum_{i\in S} \frac{n_i}{n} w_i$ (Eq. 21). The fitness proxy is what connects device selection to model quality; the pheromone dynamics are what make routing adaptive.

What would settle it

Run the same federated MLLM fine-tuning task twice, once with PSO-selected devices and once with the same number of randomly selected devices matched for energy and communication budget; if random selection matches or beats PSO on global-model accuracy over 20 rounds, the fitness proxy is not doing the work.

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Extended reading notes

Core claim

The paper's central claim is that the PSO-ACO framework improves federated MLLM deployment in edge-cloud systems: the cloud trains the global model, PSO selects a subset of edge devices that have enough resources and relevant, diverse local data, and ACO optimizes the communication paths over which the fine-tuned updates travel back for aggregation. The authors argue that this selection counteracts non-IID (non-independent and identically distributed) data by sampling a representative, diverse device subset each round, while the routing optimization reduces bandwidth use and latency, which are severe when MLLM updates are large. On an unmanned-vehicle-system use case combining the Leddar PixSet and UrbanSound8K datasets, the framework achieves 92% accuracy, an F1 score of 91%, median 88% accuracy under non-IID data, communication cost falling from 100 MB to around 40 MB per client over 20 rounds, and higher client participation than the baselines. The ablation study attributes the gains to all three components: removing PSO drops accuracy to 85%, removing ACO raises communication cost by roughly 30%, and removing edge-cloud integration drops accuracy to 83%.

Load-bearing premise

The load-bearing premise is that the weighted sum of energy use, data relevance, and data diversity in the PSO fitness function predicts which edge devices actually improve the global multimodal model's accuracy.

Editorial extensions

If this is right

If the claim holds, federated MLLM training no longer requires every device to participate: a swarm-selected subset can sustain accuracy while conserving battery and network resources.
The roughly 30% communication-cost reduction would directly address the biggest practical obstacle to MLLM federated learning, since model updates are orders of magnitude larger than for small classifiers.
The reported stable accuracy under non-IID data (median around 88%) suggests device selection on relevance and diversity can work without data-sharing or reweighting strategies.
The ablation results imply the three components are complementary: the accuracy gain comes mainly from PSO and edge-cloud integration, while the communication gain comes mainly from ACO.

Reading between the lines

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

A direct testable extension is to replace the hand-set weights $\alpha,\beta,\gamma$ with an online learned weighting or a per-device marginal-contribution estimate; the paper gives no evidence the fixed proxy is optimal.
The same hybrid decomposition (metaheuristic selection plus metaheuristic routing) should transfer to other large-model federated settings, such as parameter-efficient fine-tuning adapters on phones, where the discrete-continuous split of decisions is the same.
Because the 92% accuracy is measured on the UVS decision task rather than on the federated aggregation objective, the framework's benefit to the global MLLM itself would be established more directly by comparing global-model loss under PSO selection against random selection at equal communication budget.

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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

What you should know: this paper proposes a sensible hybrid — PSO for selecting edge devices and ACO for routing model updates in federated MLLM training over edge-cloud — but the experiments do not demonstrate the claimed gains. The central claim of improving over 'traditional FL methods' is unverified because no FL baseline appears anywhere. The evaluation compares against rule-based, CNN-LSTM, and DRL on a UVS decision task, none of which is a FL method.

What is actually new: the specific combination of PSO-based client selection and ACO-based path optimization in a federated MLLM edge-cloud setting is not in the cited literature. The problem formulation (energy, communication, non-IID) is clear, and the algorithmic descriptions are standard. That is a real but modest contribution.

Soft spots are serious. No FedAvg or other FL baseline. Accuracy is measured on the UVS decision task, not on the global MLLM objective. The figures (5–9) are missing from the text, so results can't be checked. Parameters for PSO, ACO, and the fitness weights alpha, beta, gamma are not reported, so the work is not reproducible. Reported numbers are inconsistent: abstract says 30% communication reduction, text says 100→40 MB (60%), Table 1 lists 50 MB. There are no error bars. The ACO heuristic in Section 5.3 has a sign error: ηij = 1/Bij makes low-bandwidth paths more attractive, contrary to the stated intent.

The ablation study is a nice idea but it is a table of single numbers without variance, and the removal of PSO/ACO is not tied to the FL training dynamics. The fitness proxy in Eq. (16) is unvalidated; nothing shows that the weighted combination correlates with global model accuracy.

Bottom line: the engineering idea is reasonable and worth exploring, but the paper in this form should not be accepted. It needs a complete rewrite of the experimental section: a real FedAvg baseline, non-IID partition protocol, hyperparameters, error bars, and consistent reporting. A serious editor might send it to review if the authors had a credible experiment, but as written the evidence is not there. I would desk reject now, and tell the authors to come back with a proper evaluation.

Referee Report

4 major / 6 minor

Summary. The paper proposes a hybrid PSO-ACO framework for federated fine-tuning of multimodal large language models in edge-cloud settings: PSO selects edge devices based on a weighted fitness of energy, data relevance, and diversity, and ACO routes model updates to the cloud. The authors claim 92% accuracy, 30% communication-cost reduction, and improved client participation relative to traditional FL. They evaluate on a simulated UVS use case with Leddar PixSet and UrbanSound8K, comparing against rule-based, CNN-LSTM, and DRL baselines and an ablation.

Significance. If the claims were supported, the framework would be a useful contribution to resource-aware federated MLLM deployment, and the choice of PSO/ACO is well motivated for device selection and routing. However, the central claim is not supported by the experimental design: no federated learning baseline or FL training protocol is reported, the accuracy is measured on a UVS decision task rather than the global MLLM objective, and the communication-cost numbers are mutually inconsistent. The methods sections are mostly standard metaheuristic descriptions, and no code or reproducible parameter settings are provided, so the paper currently does not meet the evidentiary bar for its headline claims.

major comments (4)

[§6.2.2, §6.3, abstract] Section 6.2.2 defines the baselines as Conventional Rule-Based, CNN-LSTM, and Deep RL, none of which is a federated learning method, and Figs. 5–9 report accuracy and communication metrics on the UVS decision-making task. The abstract's claim that the framework 'significantly improves ... accuracy of 92%, reducing communication cost by 30%, and enhancing client participation compared to traditional FL methods' is therefore untested: there is no FedAvg or equivalent FL baseline, no global-model accuracy for the federated MLLM objective, and no protocol for local fine-tuning, aggregation, non-IID partitioning, or client sampling. The reported 92% cannot be attributed to the PSO/ACO selection and routing mechanism.
[§6.3, Fig. 7; Table 1] The communication-cost results are internally inconsistent. The abstract reports a 30% reduction; the text for Fig. 7 says the PSO-ACO framework reduces cost from 100 MB to about 40 MB per client (a 60% reduction); and Table 1 lists the full-model communication cost as 50 MB. These three numbers cannot all describe the same quantity, and without an explicit communication-cost model beyond Eq. (3) the claimed savings are not reproducible.
[§5.2, Eq. (16)] The PSO fitness function is a weighted sum of energy, relevance, and diversity with weights α, β, γ, but the paper never reports the values of these weights or validates that minimizing this proxy improves the global MLLM. The 92% accuracy is reported on a UVS decision task, not on the federated MLLM objective, so the correlation between the selection proxy and model accuracy is unestablished. If the proxy is mis-specified, PSO selects the wrong devices and the central accuracy claim does not follow.
[§6.4, Table 1] The ablation study presents single values with no error bars, number of runs, or statistical tests, and it omits all PSO/ACO hyperparameters (inertia ω, acceleration coefficients c1,c2, evaporation rate ρ, and exponents α,β in Eq. (19)). The row 'Without PSO' has the same communication cost (50 MB) as the full model, so the reported differences cannot be separated from noise or parameter choice, and the ablation does not isolate the FL-specific contribution of either swarm component.

minor comments (6)

[Eq. (3)] The quantity Mi/Bi · di mixes units (model size divided by bandwidth, then multiplied by distance); the intended model should either define di as a normalized coefficient or state units explicitly.
[§5.3] The heuristic ηij for bandwidth is defined as 1/Bij while the text says higher-bandwidth paths should be preferred; as written, lower-bandwidth links receive larger heuristic values, which contradicts the stated goal. If higher bandwidth is better, ηij should be proportional to Bij (or the inverse should be used for latency only).
[§2.1, Eq. (1)] The sentence 'Ecomm and Etrain represents the energy and time required to transmit model updates' confuses energy and time; please clarify the roles of Etrain, Ecomm, and Etime.
[§6.3, Figs. 5–9] Figures 5–9 are referenced only through their captions and accompanying text; the actual plot images are missing from the manuscript, and none of the reported metrics include error bars or confidence intervals.
[§5.2, Eq. (16)] The diversity term γ Σ Di is minimized together with energy and relevance; if high diversity is desired, the diversity term should enter negatively or as an inverse so that the optimization direction matches the stated non-IID objective.
[§6.3 vs §6.2.2] Section 6.3 refers to 'federated learning approaches' although Section 6.2.2 does not include any FL baseline; please align the terminology with the actual comparison class.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the swarm-intelligence components are standard algorithms and the reported gains are empirical claims, not consequences of the paper's definitions.

full rationale

The paper's derivation chain is not circular. PSO device selection and ACO path optimization are standard metaheuristics with externally cited pedigrees; the fitness and update equations (Eqs. 16-20) are stated directly and do not import results from the authors' prior work. The claimed 92% accuracy, communication-cost reduction, and client-participation improvements are presented as simulation outcomes on the PixSet/UrbanSound8K benchmark, not as re-statements of the optimization objectives. The fitness function in Eq. (16) uses energy, relevance, and diversity proxies; while the weights alpha/beta/gamma and the definitions of Ri and Di are unspecified, this is a reproducibility and validation weakness rather than a circular reduction, because accuracy is not the quantity being optimized. The absence of a standard FL baseline and the inconsistency between the abstract's 30% and Fig. 7's 60% communication reductions are serious correctness concerns, but they do not make any claimed result equivalent to its inputs by construction. Self-citations appear only in related-work and future-work contexts and are not load-bearing for the central claim.

Assumptions & free parameters 4 free parameters · 2 assumptions · 0 invented entities

The report's core claims rest on a small set of tunable parameters and two domain assumptions about the simulation and the metaheuristics. No new physical entities or formal axioms are introduced.

free parameters (4)

alpha, beta, gamma trade-off weights in Eqs. (6) and (16) = not reported
They control how energy, latency, data relevance, and diversity trade off in device selection; the reported 92% accuracy may depend on these choices.
ACO exponents alpha, beta and evaporation rate rho = not reported
Standard ACO parameters that determine path selection and pheromone update; not specified in the paper.
PSO inertia weight omega and acceleration coefficients c1, c2 = not reported
Standard PSO parameters; not specified in the paper.
Communication cost model constants (initial 100 MB per client) = 100 MB initial, 40 MB final (text) / 50 MB (Table 1)
The communication model in Eq. (3) needs per-unit constants; the paper reports a final cost without giving the parameters.

assumptions (2)

domain assumption PSO and ACO converge to near-optimal solutions for device selection and path routing in dynamic edge-cloud networks.
The paper relies on the standard convergence behavior of these metaheuristics; no convergence guarantee is proven for the specific non-convex mixed-integer problem.
domain assumption The simulated UVS environment with 50 to 200 devices and the Leddar PixSet and UrbanSound8K datasets approximates real MLLM FL deployment.
This underlies all experimental claims; no validation of simulation fidelity is provided.

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Cite this review

Pith. "Pith review of A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environments." pith.science (2026). https://pith.science/paper/EXZ7NAMW

@misc{pith2026250210419,
  author       = {Pith},
  title        = {Pith review of: A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environments},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EXZ7NAMW}},
  note         = {Machine review of arXiv:2502.10419}
}

read the original abstract

The combination of Federated Learning (FL), Multimodal Large Language Models (MLLMs), and edge-cloud computing enables distributed and real-time data processing while preserving privacy across edge devices and cloud infrastructure. However, the deployment of MLLMs in FL environments with resource-constrained edge devices presents significant challenges, including resource management, communication overhead, and non-IID data. To address these challenges, we propose a novel hybrid framework wherein MLLMs are deployed on edge devices equipped with sufficient resources and battery life, while the majority of training occurs in the cloud. To identify suitable edge devices for deployment, we employ Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) is utilized to optimize the transmission of model updates between edge and cloud nodes. This proposed swarm intelligence-based framework aims to enhance the efficiency of MLLM training by conducting extensive training in the cloud and fine-tuning at the edge, thereby reducing energy consumption and communication costs. Our experimental results show that the proposed method significantly improves system performance, achieving an accuracy of 92%, reducing communication cost by 30%, and enhancing client participation compared to traditional FL methods. These results make the proposed approach highly suitable for large-scale edge-cloud computing systems.

Figures

Figures reproduced from arXiv: 2502.10419 by the authors.

**Figure 1.** Edge-Cloud-based MLLM Architecture with Federated Learning for UAV Networks To address the limitations of edge devices, which often lack the necessary resources and capacities for large models like MLLMs, we introduce a framework that deploys pretrained MLLMs on these devices for fine-tuning, while primary training is conducted in the cloud. In this framework, Particle Swarm Optimization (PSO) is employed to effici… view at source ↗

**Figure 2.** Architectural Overview of MLLM. 3.1 BACKBONE OF MLLM LLM Foundation: The core of an MLLM is an LLM such as GPT, PaLM, or BERT, trained on extensive text datasets Cui et al. (2024). The language model is typically a transformer-based model with self-attention mechanisms that enable it to understand and generate text Islam et al. (2024). 5 [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Overview of the Proposed Framework for Optimizing [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

Figures from the paper (6 more)

**Figure 4.** Figure 4: The Architecture of the Use Case Solution [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]

**Figure 5.** Figure 5: Performance Metrics Comparison for Different Models [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]

**Figure 6.** Figure 6: Convergence Analysis (Training Loss over Epochs) [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]

**Figure 7.** Figure 7: Communication Cost Reduction over Rounds [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]

**Figure 8.** Figure 8: Impact of Non-IID Data Distribution on Model Accuracy [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]

**Figure 9.** Figure 9: Client Participation Rate over Rounds for All Models [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.

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Abualigah, L., Falcone, D., & Forestiero, A. (2023). Swarm intelligence to face IoT challenges. Computational Intelligence and Neuroscience, 2023, 4254194. Akhtarshenas, A., Vahedifar, M. A., Ayoobi, N., Maham, B., Alizadeh, T., Ebrahimi, S., & L ´opez-P´erez, D. (2024). Federated learning: A cutting-edge survey of the latest advancements and applications...

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