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REVIEW 3 major objections 6 minor 54 references

Energy-Efficient Federated Learning for AIoT using Clustering Methods

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper claims that one-time clustering of clients by label distribution makes federated learning reach target accuracy with lower total energy than per-round adaptive selection.

desk verdict A serious energy-measurement study of one-time client clustering for FL, but RepClust's per-round client count is unclear and G is tuned on the metric. read the letter →

arxiv 2505.09704 v1 pith:RIUGBCZI submitted 2025-05-14 cs.LG cs.AI

Roberto Pereira , Fernanda Famá , Charalampos Kalalas , Paolo Dini This is my paper · ORCID
classification cs.LGcs.AI
keywords federatedlearningclientselectionclusteringenergyefficiencylabeldistributionAIoTnon-IIDdatarepulsive
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

This paper proposes replacing the per-round active client selection of federated learning with a single clustering step performed before training starts. Clients are grouped by their label distributions so that each round's participants jointly represent the whole data distribution. The authors argue that local training, not communication, dominates the energy budget of FL on AIoT devices, so selecting representative clients that converge faster saves more energy than merely reducing communication rounds. Their two clustering methods, SimClust and RepClust, reach comparable or higher accuracy than recent selection approaches while spending far less on pre-processing, with RepClust the best among methods that do not share local data.

What carries the argument

The machinery is a one-time clustering of the label-distribution vectors of all clients, computed before federated training and then held fixed. SimClust clusters similar label distributions so that stratified sampling from clusters yields diversity across the data space; RepClust solves a multi-objective dispersion problem, maximizing intra-cluster pairwise distance while minimizing inter-cluster distance with equal cluster sizes, using a heuristic swap algorithm. This transforms client selection from an online optimization repeated every round into a fixed partition, moving the cost into a negligible pre-processing step while ensuring each round's sampled clients collectively cover the label space.

What would settle it

Run the same protocol on a dataset whose local label distributions change every 50 rounds, or compute the initial clustering from deliberately corrupted label estimates, and compare energy-to-accuracy against a per-round adaptive selector such as FedCor; if the clustering methods lose their advantage whenever the initial distributions become stale, the one-time-clustering claim is falsified.

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Extended reading notes

Core claim

The central discovery is that the most energy-consuming part of federated learning on constrained AIoT devices is local training, and that a one-time, pre-training clustering of clients by label distribution can make every communication round's selected clients jointly approximate the global label distribution. SimClust groups similar clients, using k-means with symmetrized KL divergence, and draws one client per group per round; RepClust forms equal-sized groups that are internally diverse and mutually similar, then engages a whole group per round. The paper reports that these methods need fewer rounds to reach target accuracy in heterogeneous settings than random sampling or FedCor's Gaussian-process selection, while spending almost nothing on pre-processing, so total energy falls. Among baselines that do not share local data, RepClust achieves the highest accuracy on F-MNIST, CIFAR-10, and CIFAR-100 under equal energy budgets.

Load-bearing premise

The clusters are computed once from label distributions known before training, and the paper assumes those distributions do not change over the 500 training rounds; if the data drifts or the estimates are wrong, the sampled clients are no longer representative and the energy and accuracy results would not transfer.

Editorial extensions

If this is right

  • In heterogeneous settings with location-dependent partitions, the clustering methods reach target accuracy in the same or fewer rounds than FedCor, making them the lowest-energy option.
  • Because clustering is executed only once, pre-processing energy is negligible compared with active selection methods that train a Gaussian process or rank clients every round.
  • RepClust's optimal number of clusters stays relatively stable across random seeds, so its energy profile is less sensitive to the choice of cluster count than SimClust's.
  • Under differential privacy noise added to label distributions, RepClust retains a non-random clustering solution even when SimClust degrades toward random assignment, suggesting the approach can tolerate privacy masking.
  • The authors note the clustering pre-processing is compatible with further efficiency techniques such as over-the-air computing, quantization, and pruning, which could reduce server-side energy on top of the sampling savings.

Reading between the lines

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

  • If local training dominates the energy budget, the relative benefit of one-time clustering should grow as models get shallower or local epochs increase; on deeper models the communication fraction rises, so clustering's advantage may narrow.
  • The fixed-cluster assumption suggests a testable extension: re-clustering periodically or when drift is detected could extend the method to non-stationary AIoT data at a small additional pre-processing cost.
  • A practical deployment could have each device send a differentially private label histogram before clustering; the paper's Appendix C results imply RepClust would withstand more noise than SimClust before cluster structure is lost.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. This paper proposes two one-time clustering-based client selection schemes for federated learning in AIoT settings: SimClust, which groups clients with similar label distributions and samples across groups, and RepClust, which forms equal-size clusters of maximally diverse clients and selects one entire cluster per round. The authors build an energy model covering pre-processing, local training, and communication, and compare their methods against random sampling, FedCor, PowerD, DELTA, ClustLowVar, and FLIS on F-MNIST, CIFAR-10, and CIFAR-100. The central claim is that one-time clustering achieves accuracy comparable to or better than active selection while consuming less total energy.

Significance. If the results hold, the paper would make a useful contribution: a single pre-training clustering step could replace per-round active client selection and reduce total energy without sacrificing accuracy. The strengths of the paper are its careful energy decomposition, the inclusion of several recent baselines, multi-seed experiments, code availability, and a differential-privacy appendix. However, the main quantitative conclusions currently rest on two unresolved points: the number of clients actually trained per round under RepClust is not fixed to K=10, and the number of clusters G is selected per scenario on the evaluation metric. These issues must be resolved before the energy-efficiency claim can be accepted.

major comments (3)
  1. [Section III-B, Section V-A, Section V-D] In Section III-B, RepClust is described as selecting one whole partition per round, and constraint (3) imposes |C_g| = L/G for all clusters. With L=100 clients and G=20, the value reported as best for RepClust in Fig. 6(b), this yields 5 participating clients per round, not the K=10 stated in Section V-A and claimed as constant K=10 in the Fig. 6 caption. The manuscript does not describe how exactly 10 clients are selected for RepClust when G differs from 10. Because local training and communication energy both scale with the number of participating clients, the reported energy advantage of RepClust could be a structural artifact of training fewer clients per round rather than evidence of better selection. Please specify the sampling rule for every G and ensure the number of active clients is identical across all compared methods, or analyze per-client energy separately.
  2. [Section V-A and Tables I-II] For the proposed clustering solutions, G is chosen as the value in {2,5,10,20,25,50} that minimizes the overall energy costs for each scenario. This is equivalent to tuning the free parameter on the test scenario using the evaluation metric, so the reported energy savings and accuracy results are partly an artifact of this test-set selection. The experiments should use a validation-based or fixed G across scenarios, or the results should be reported as a sensitivity analysis rather than as the expected performance of the method.
  3. [Section V-E and Table II] The statement that, among methods not sharing local data, RepClust achieves the highest accuracy across all datasets is not borne out by Table II for the CIFAR-10 final accuracy column: SimClust reaches 42.79% versus 41.80% for RepClust. If the claim is intended to apply only under the 60%, 80%, and 100% energy budgets, it should be stated explicitly; as written, it overstates the result.
minor comments (6)
  1. [Section V-A] The text says 'we perform 10 rounds of local training' but elsewhere refers to epochs; this should be corrected to '10 local epochs' for consistency.
  2. [Section III-C] The complexity analysis uses K for the total number of clients and G for the number of groups, but K was defined earlier as the per-round number of selected clients; either reuse L for the total number of clients or define the notation locally.
  3. [Section IV-B] The communication-energy equation has mismatched parentheses and the sentence describing P_down and P_up appears to swap the roles of uplink and downlink; please check the notation against the formulas in Refs. [4] and [48].
  4. [Appendix C] The Gaussian noise added to label distributions is not calibrated to sensitivity or to a stated privacy budget, so the formal differential-privacy guarantee is not established; either provide the privacy analysis or soften the wording to an empirical robustness study.
  5. [References] Reference [28] contains an odd date field 'oct 5555' that should be corrected to the actual publication date.
  6. [Figure 6] The caption states 'constant K=10' but, given the RepClust selection rule and constraint (3), it is not clear how K=10 is realized for G values other than 10; this relates to the first major comment and should be clarified in the caption or the experiment setup.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; the headline energy/accuracy claims are empirical comparisons against external baselines.

full rationale

The paper's main results are measured, not derived: accuracy is evaluated on held-out test splits and energy is computed from Codecarbon hardware tracking plus an IEEE 802.11ax communication model, with comparisons to Rand, FedCor, PowerD, DELTA, FLIS, and ClustLowVar on F-MNIST, CIFAR-10, and CIFAR-100. Neither quantity is defined by the clustering assignment, so the ranking of methods is not forced by construction. The choice of k-Means with symmetrized KL divergence is justified by the authors' own prior study [29], but that self-citation is a design heuristic and is not load-bearing for the benchmark comparison. Selecting G to minimize the reported energy metric is a tuning/selection concern that can inflate the methods' apparent energy efficiency, and the paper does not fully specify how RepClust enforces exactly K=10 participants when clusters have size L/G; however, these are fairness/validity issues rather than reductions of the central claim to its inputs. The acknowledged no-drift assumption (footnote 5) is a scope limitation, not a circular step. Overall, no enumerated circularity pattern applies, so the core empirical claim is self-contained.

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

All reported energy advantages rest on the static label-distribution assumption, the representativeness of the energy model, and the effectiveness of Algorithm 1; G is selected post hoc to minimize the reported energy metric.

free parameters (2)
  • Number of clusters G = selected per scenario from {2,5,10,20,25,50} to minimize total energy
    Section V states G is chosen as the value that minimizes overall energy costs; the reported energy and accuracy results for SimClust and RepClust therefore depend on an oracle-like choice of G for the test scenario.
  • RepClust search budget S = not reported
    Algorithm 1 takes S (maximum search interactions) and an iteration count as inputs, but the paper never specifies their values, leaving cluster quality partly determined by an unreported hand-set parameter.
assumptions (4)
  • domain assumption Local label distributions are available to the server and do not change over the 500 training rounds.
    The entire one-time clustering procedure depends on static label distributions; Section III, footnote 5 concedes temporal drift is excluded. If false, clusters are stale and the reported gains are not guaranteed.
  • domain assumption Energy measured with Codecarbon on the authors' hardware plus the 802.11ax model is a faithful proxy for energy on AIoT devices.
    Energy conclusions rely on the representativeness of the measurement setup described in Section IV; no measurements on actual constrained edge hardware are reported.
  • domain assumption The adapted repulsive clustering heuristic from [39] yields partitions close enough to the NP-hard optimum of problem (3).
    Algorithm 1 is presented without guarantees; the claimed benefits of RepClust depend on the heuristic's local search finding good diverse groups.
  • standard math Standard k-Means with symmetrized KL divergence converges to a useful partition of label distributions.
    SimClust relies on k-Means, whose convergence to a local optimum is standard; the statistical interpretation of KL divergence is used to define the distance metric.

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Pith. "Pith review of Energy-Efficient Federated Learning for AIoT using Clustering Methods." pith.science (2026). https://pith.science/paper/RIUGBCZI

@misc{pith2026250509704,
  author       = {Pith},
  title        = {Pith review of: Energy-Efficient Federated Learning for AIoT using Clustering Methods},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RIUGBCZI}},
  note         = {Machine review of arXiv:2505.09704}
}
read the original abstract

While substantial research has been devoted to optimizing model performance, convergence rates, and communication efficiency, the energy implications of federated learning (FL) within Artificial Intelligence of Things (AIoT) scenarios are often overlooked in the existing literature. This study examines the energy consumed during the FL process, focusing on three main energy-intensive processes: pre-processing, communication, and local learning, all contributing to the overall energy footprint. We rely on the observation that device/client selection is crucial for speeding up the convergence of model training in a distributed AIoT setting and propose two clustering-informed methods. These clustering solutions are designed to group AIoT devices with similar label distributions, resulting in clusters composed of nearly heterogeneous devices. Hence, our methods alleviate the heterogeneity often encountered in real-world distributed learning applications. Throughout extensive numerical experimentation, we demonstrate that our clustering strategies typically achieve high convergence rates while maintaining low energy consumption when compared to other recent approaches available in the literature.

Figures

Figures reproduced from arXiv: 2505.09704 by the authors.

Figure 1
Figure 1. Percentage of the total energy consumed during training (solid) [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Comparison between traditional device random sampling (first [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Illustration of different data partition schemes for CIFAR-10 datasets for fixed [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Energy profiles of client selection mechanisms (represented by different colors) divided into learning (solid color at the bottom), [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: Accuracy plots for (a) CIFAR and (b) F-MNIST for different [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Impact in energy and accuracy of the number of clusters [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 8
Figure 8. Figure 8: Accuracy plots for (a) CIFAR and (b) F-MNIST for different [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 7
Figure 7. Figure 7: Energy consumed to perform clustering on CIFAR-10 under [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]

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Reviewed August 15, 2026 · model on record in the stance chip above.