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arxiv: 2605.11010 · v1 · submitted 2026-05-10 · 💻 cs.LG

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A Comparative Study of Federated Learning Aggregation Strategies under Homogeneous and Heterogeneous Data Distributions

Antonios Makris, Christos Dousis, Emmanouil Kritharakis, Konstantinos Tserpes, Stavros Bouras

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Pith reviewed 2026-05-13 07:22 UTC · model grok-4.3

classification 💻 cs.LG
keywords federated learningaggregation strategiesdata heterogeneitynon-IID dataimage classificationmodel aggregationperformance comparison
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The pith

Federated learning aggregation strategies exhibit distinct trade-offs that vary with data homogeneity and dataset characteristics.

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

This paper runs side-by-side experiments on standard image classification tasks to compare how common server-side aggregation methods combine client updates when data is either evenly shared or unevenly partitioned across devices. It tracks centralized accuracy and loss together with wall-clock costs for aggregation, training rounds, and communication. The central observation is that each strategy's relative strengths shift depending on the amount of data heterogeneity and the underlying dataset, so no method is universally superior.

Core claim

The paper shows through controlled experiments that aggregation strategies display distinct trade-offs in accuracy, loss, and system efficiency metrics across homogeneous and heterogeneous data distributions, with effectiveness tied to specific dataset properties and operating conditions.

What carries the argument

Server-side combination of local model updates using different aggregation rules, tested for sensitivity to non-IID data partitions on image classification benchmarks.

If this is right

  • Strategy selection must account for the expected degree of data heterogeneity rather than defaulting to a single method.
  • Efficiency metrics such as communication time can shift rankings among strategies even when accuracy remains comparable.
  • Dataset-specific characteristics can override general rules about which aggregation rule performs best.
  • Practical deployments should benchmark multiple strategies under their actual data distribution before fixing one.

Where Pith is reading between the lines

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

  • Hybrid or adaptive aggregation rules that detect local data statistics could reduce the observed trade-offs.
  • The same experimental design could be applied to non-vision tasks such as language modeling or sensor data to test whether the pattern generalizes.
  • Extreme heterogeneity regimes not covered in the benchmarks might require entirely different aggregation logic.

Load-bearing premise

The selected image classification benchmarks and the tested levels of data heterogeneity are representative of real federated deployments.

What would settle it

Repeating the exact protocol on a dataset with substantially different statistics or with more extreme heterogeneity levels and observing that the reported trade-off patterns disappear.

Figures

Figures reproduced from arXiv: 2605.11010 by Antonios Makris, Christos Dousis, Emmanouil Kritharakis, Konstantinos Tserpes, Stavros Bouras.

Figure 1
Figure 1. Figure 1: Data heterogeneity in the 10-client FL setup on CIFAR [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Centralized accuracy across datasets for all aggregation strategies under IID and non-IID data distributions. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
read the original abstract

Federated Learning has emerged as a transformative paradigm for collaborative machine learning across distributed environments. However, its performance is strongly influenced by the aggregation strategy used to combine local model updates at the server, which directly affects learning performance, robustness, and system behavior. This work presents a comprehensive experimental comparison of widely used federated aggregation strategies under both homogeneous and heterogeneous data distributions. Using benchmark image classification datasets, we analyze how different aggregation mechanisms respond to varying degrees of data heterogeneity, examining their impact on centralized accuracy and loss, and system-level efficiency metrics, including aggregation, training, and communication time. The results demonstrate that aggregation strategies exhibit distinct trade-offs across datasets and data distributions, with their effectiveness varying according to dataset characteristics and operating conditions.

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.

Referee Report

2 major / 2 minor

Summary. The paper conducts a comprehensive experimental comparison of common federated learning aggregation strategies under both homogeneous and heterogeneous data distributions. Using standard image classification benchmarks, it evaluates impacts on centralized accuracy and loss as well as system-level metrics (aggregation time, training time, communication time), concluding that the strategies exhibit distinct trade-offs whose effectiveness depends on dataset characteristics and the degree of data heterogeneity.

Significance. If the empirical results prove robust, the study supplies practical, side-by-side evidence that can help practitioners choose aggregation methods according to expected data skew and efficiency constraints. It contributes an organized observational catalog of performance differences across multiple metrics rather than a new theoretical derivation or single-strategy improvement.

major comments (2)
  1. [Methods] Methods / Experimental Setup: The manuscript provides no information on the number of independent runs, random-seed averaging, or statistical significance testing for the reported accuracy, loss, and timing differences. Without these, the central claim that strategies 'exhibit distinct trade-offs' rests on single-point observations whose variability cannot be assessed.
  2. [Experimental Setup] Data Heterogeneity Generation: The precise procedure and parameters used to create heterogeneous partitions (e.g., Dirichlet concentration values, feature-skew mechanisms) are not stated. This detail is load-bearing for interpreting how each aggregation strategy responds to 'varying degrees of data heterogeneity'.
minor comments (2)
  1. [Abstract] Abstract: The list of compared aggregation strategies is not named; explicitly enumerating FedAvg, FedProx, SCAFFOLD, etc., would improve immediate clarity.
  2. [Results] Results presentation: Figures and tables should include error bars or standard deviations and consistent axis scaling across datasets to make the claimed trade-offs visually verifiable.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We agree that additional methodological details are needed to strengthen the reproducibility and interpretability of our empirical comparisons. Below we address each major comment and indicate the revisions we will make.

read point-by-point responses

  1. Referee: [Methods] Methods / Experimental Setup: The manuscript provides no information on the number of independent runs, random-seed averaging, or statistical significance testing for the reported accuracy, loss, and timing differences. Without these, the central claim that strategies 'exhibit distinct trade-offs' rests on single-point observations whose variability cannot be assessed.

    Authors: We acknowledge the validity of this observation. The original experiments were performed with a single run per configuration, which limits assessment of variability. In the revised manuscript we will rerun all experiments using five independent random seeds, report mean and standard deviation for accuracy, loss, and timing metrics, and include paired t-tests (or Wilcoxon tests where normality assumptions fail) to establish statistical significance of the observed differences between aggregation strategies. These additions will be placed in a new subsection of the experimental setup and reflected in updated tables and figures. revision: yes

  2. Referee: [Experimental Setup] Data Heterogeneity Generation: The precise procedure and parameters used to create heterogeneous partitions (e.g., Dirichlet concentration values, feature-skew mechanisms) are not stated. This detail is load-bearing for interpreting how each aggregation strategy responds to 'varying degrees of data heterogeneity'.

    Authors: We agree that the exact data-partitioning procedure must be fully specified. In the revised version we will add a dedicated paragraph describing the heterogeneity generation: label skew is induced via Dirichlet distribution with concentration parameters α ∈ {0.1, 0.5, 1.0, 10.0} (lower α corresponds to higher heterogeneity); feature skew is introduced by applying random rotations and color jitter with fixed seeds per client. We will also provide the exact client-to-class mapping tables and the code snippet used to generate the partitions so that readers can reproduce the exact degree of heterogeneity. revision: yes

Circularity Check

0 steps flagged

No significant circularity: purely experimental comparison with no derivations or self-referential predictions

full rationale

The paper is a comparative experimental study of federated learning aggregation strategies on benchmark image classification datasets under varying data distributions. It reports direct measurements of accuracy, loss, and efficiency metrics without any mathematical derivations, parameter fitting presented as prediction, or load-bearing self-citations. The central claims rest on empirical results against external benchmarks, with no equations or internal reductions that could create circularity by construction. This matches the default expectation for non-circular papers.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a purely empirical comparative study. No new mathematical derivations, free parameters, axioms, or invented entities are introduced.

pith-pipeline@v0.9.0 · 5438 in / 1037 out tokens · 45243 ms · 2026-05-13T07:22:19.964157+00:00 · methodology

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Reference graph

Works this paper leans on

40 extracted references · 40 canonical work pages · 3 internal anchors

  1. [1]

    An overview of implementing security and privacy in federated learning,

    K. Hu, S. Gong, Q. Zhang, C. Seng, M. Xia, and S. Jiang, “An overview of implementing security and privacy in federated learning,”Artificial intelligence review, vol. 57, no. 8, p. 204, 2024

    work page 2024

  2. [2]

    Coevolution: A comprehensive trustworthy framework for connected machine learning and secure interconnected ai solutions,

    A. Makris, A. Fournaris, A. Aghaie, I. Arakas, A. M. Anaxagorou, I. Arapakis, D. Bacciu, B. Biggio, G. Bouloukakis, S. Bouraset al., “Coevolution: A comprehensive trustworthy framework for connected machine learning and secure interconnected ai solutions,” in2025 IEEE International Conference on Cyber Security and Resilience (CSR). IEEE, 2025, pp. 838–845

    work page 2025

  3. [3]

    Communication-efficient learning of deep networks from decentralized data,

    H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” inProceedings of the 20th International Conference on Artificial Intelligence and Statistics. PMLR, 2017, pp. 1273–1282

    work page 2017

  4. [4]

    A survey on federated learning,

    C. Zhang, Y . Xie, H. Bai, B. Yu, W. Li, and Y . Gao, “A survey on federated learning,”Knowledge-Based Systems, vol. 216, p. 106775, 2021

    work page 2021

  5. [5]

    Federated Learning with Personalization Layers

    M. G. Arivazhagan, V . Aggarwal, A. K. Singh, and S. Choud- hary, “Federated learning with personalization layers,”arXiv preprint arXiv:1912.00818, 2019

    work page internal anchor Pith review arXiv 1912

  6. [6]

    Non-iid data and continual learning processes in federated learning: A long road ahead,

    M. F. Criado, F. E. Casado, R. Iglesias, C. V . Regueiro, and S. Barro, “Non-iid data and continual learning processes in federated learning: A long road ahead,”Information Fusion, vol. 88, pp. 263–280, 2022

    work page 2022

  7. [7]

    Efficient and light-weight federated learning via asynchronous dis- tributed dropout,

    C. Dun, M. Hipolito, C. Jermaine, D. Dimitriadis, and A. Kyrillidis, “Efficient and light-weight federated learning via asynchronous dis- tributed dropout,” inInternational Conference on Artificial Intelligence and Statistics. PMLR, 2023, pp. 6630–6660

    work page 2023

  8. [8]

    Adaptive federated optimization,

    S. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Kone ˇcn`y, S. Kumar, and H. B. McMahan, “Adaptive federated optimization,”arXiv preprint arXiv:2003.00295, 2020

    work page arXiv 2003

  9. [9]

    Fedtrip: A resource-efficient federated learning method with triplet regularization,

    X. Li, M. Liu, S. Sun, Y . Wang, H. Jiang, and X. Jiang, “Fedtrip: A resource-efficient federated learning method with triplet regularization,” in2023 IEEE International Parallel and Distributed Processing Sympo- sium (IPDPS). IEEE, 2023, pp. 809–819

    work page 2023

  10. [10]

    Hyperparameter impact on compu- tational efficiency in federated edge learning,

    J.-F. Dollinger, M. Zghalet al., “Hyperparameter impact on compu- tational efficiency in federated edge learning,” in2024 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2024, pp. 0849–0854

    work page 2024

  11. [11]

    Measuring the

    T.-M. H. Hsu, H. Qi, and M. Brown, “Measuring the effects of non- identical data distribution for federated visual classification,”arXiv preprint arXiv:1909.06335, 2019

    work page arXiv 1909

  12. [12]

    Client selection in federated learning: Principles, challenges, and opportunities,

    L. Fu, H. Zhang, G. Gao, M. Zhang, and X. Liu, “Client selection in federated learning: Principles, challenges, and opportunities,”IEEE Internet of Things Journal, vol. 10, no. 24, pp. 21 811–21 819, 2023

    work page 2023

  13. [13]

    Network-aware optimization of distributed learning for fog computing,

    S. Wang, Y . Ruan, Y . Tu, S. Wagle, C. G. Brinton, and C. Joe-Wong, “Network-aware optimization of distributed learning for fog computing,” IEEE/ACM Transactions on Networking, vol. 29, no. 5, pp. 2019–2032, 2021

    work page 2019

  14. [14]

    Over-the-air federated learning with joint adaptive computation and power control,

    H. Yang, P. Qiu, J. Liu, and A. Yener, “Over-the-air federated learning with joint adaptive computation and power control,” in2022 IEEE International Symposium on Information Theory (ISIT). IEEE, 2022, pp. 1259–1264

    work page 2022

  15. [15]

    Efficient asynchronous federated learning research in the internet of vehicles,

    Z. Yang, X. Zhang, D. Wu, R. Wang, P. Zhang, and Y . Wu, “Efficient asynchronous federated learning research in the internet of vehicles,” IEEE Internet of Things Journal, vol. 10, no. 9, pp. 7737–7748, 2022

    work page 2022

  16. [16]

    Secure and efficient federated learning for smart grid with edge-cloud collaboration,

    Z. Su, Y . Wang, T. H. Luan, N. Zhang, F. Li, T. Chen, and H. Cao, “Secure and efficient federated learning for smart grid with edge-cloud collaboration,”IEEE Transactions on Industrial Informatics, vol. 18, no. 2, pp. 1333–1344, 2021

    work page 2021

  17. [17]

    Throughput-optimal topology design for cross-silo federated learning,

    O. Marfoq, C. Xu, G. Neglia, and R. Vidal, “Throughput-optimal topology design for cross-silo federated learning,”Advances in Neural Information Processing Systems, vol. 33, pp. 19 478–19 487, 2020

    work page 2020

  18. [18]

    Federated learning with compression: Unified analysis and sharp guarantees,

    F. Haddadpour, M. M. Kamani, A. Mokhtari, and M. Mahdavi, “Federated learning with compression: Unified analysis and sharp guarantees,” in International Conference on Artificial Intelligence and Statistics. PMLR, 2021, pp. 2350–2358

    work page 2021

  19. [19]

    Uveqfed: Universal vector quantization for federated learning,

    N. Shlezinger, M. Chen, Y . C. Eldar, H. V . Poor, and S. Cui, “Uveqfed: Universal vector quantization for federated learning,”IEEE Transactions on Signal Processing, vol. 69, pp. 500–514, 2020

    work page 2020

  20. [20]

    Fetchsgd: Communication-efficient feder- ated learning with sketching,

    D. Rothchild, A. Panda, E. Ullah, N. Ivkin, I. Stoica, V . Braverman, J. Gonzalez, and R. Arora, “Fetchsgd: Communication-efficient feder- ated learning with sketching,” inInternational Conference on Machine Learning. PMLR, 2020, pp. 8253–8265

    work page 2020

  21. [21]

    Differential privacy meets federated learning under communication constraints,

    N. Mohammadi, J. Bai, Q. Fan, Y . Song, Y . Yi, and L. Liu, “Differential privacy meets federated learning under communication constraints,”IEEE Internet of Things Journal, vol. 9, no. 22, pp. 22 204–22 219, 2021

    work page 2021

  22. [22]

    Optimal user-edge assignment in hierarchical federated learning based on statistical properties and network topology constraints,

    N. Mhaisen, A. A. Abdellatif, A. Mohamed, A. Erbad, and M. Guizani, “Optimal user-edge assignment in hierarchical federated learning based on statistical properties and network topology constraints,”IEEE Trans- actions on Network Science and Engineering, vol. 9, no. 1, pp. 55–66, 2021

    work page 2021

  23. [23]

    Sageflow: Robust federated learning against both stragglers and adversaries,

    J. Park, D.-J. Han, M. Choi, and J. Moon, “Sageflow: Robust federated learning against both stragglers and adversaries,”Advances in neural information processing systems, vol. 34, pp. 840–851, 2021

    work page 2021

  24. [24]

    Zero-knowledge proof-based practical federated learning on blockchain,

    Z. Xing, Z. Zhang, M. Li, J. Liu, L. Zhu, G. Russello, and M. R. Asghar, “Zero-knowledge proof-based practical federated learning on blockchain,”arXiv preprint arXiv:2304.05590, 2023

    work page arXiv 2023

  25. [25]

    Label noise analysis meets adversarial training: A defense against label poisoning in federated learning,

    E. Hallaji, R. Razavi-Far, M. Saif, and E. Herrera-Viedma, “Label noise analysis meets adversarial training: A defense against label poisoning in federated learning,”Knowledge-based systems, vol. 266, p. 110384, 2023

    work page 2023

  26. [26]

    16 federated knowl- edge distillation,

    H. Seo, J. Park, S. Oh, M. Bennis, and S.-L. Kim, “16 federated knowl- edge distillation,”Machine Learning and Wireless Communications, vol. 457, 2022

    work page 2022

  27. [27]

    Practical Secure Aggregation for Federated Learning on User-Held Data

    K. Bonawitz, V . Ivanov, B. Kreuter, A. Marcedone, H. B. McMa- han, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for federated learning on user-held data,”arXiv preprint arXiv:1611.04482, 2016

    work page Pith review arXiv 2016

  28. [28]

    Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering

    E. Kritharakis, D. Jakovetic, A. Makris, and K. Tserpes, “Robust feder- ated learning under adversarial attacks via loss-based client clustering,” arXiv preprint arXiv:2508.12672, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  29. [29]

    Robust aggregation for federated learning,

    K. Pillutla, S. M. Kakade, and Z. Harchaoui, “Robust aggregation for federated learning,”IEEE Transactions on Signal Processing, vol. 70, pp. 1142–1154, 2022

    work page 2022

  30. [30]

    Federated learning: Challenges, methods, and future directions,

    T. Li, A. K. Sahu, A. Talwalkar, and V . Smith, “Federated learning: Challenges, methods, and future directions,”IEEE signal processing magazine, vol. 37, no. 3, pp. 50–60, 2020

    work page 2020

  31. [31]

    Byzantine-robust distributed learning: Towards optimal statistical rates,

    D. Yin, Y . Chen, R. Kannan, and P. Bartlett, “Byzantine-robust distributed learning: Towards optimal statistical rates,” inInternational conference on machine learning. Pmlr, 2018, pp. 5650–5659

    work page 2018

  32. [32]

    Machine learning with adversaries: Byzantine tolerant gradient descent,

    P. Blanchard, E. M. El Mhamdi, R. Guerraoui, and J. Stainer, “Machine learning with adversaries: Byzantine tolerant gradient descent,”Advances in neural information processing systems, vol. 30, 2017

    work page 2017

  33. [33]

    The hidden vulnerability of distributed learning in byzantium,

    R. Guerraoui, S. Rouaultet al., “The hidden vulnerability of distributed learning in byzantium,” inInternational conference on machine learning. PMLR, 2018, pp. 3521–3530

    work page 2018

  34. [34]

    Fedgreed: A byzantine-robust loss-based aggregation method for federated learning,

    E. Kritharakis, A. Makris, D. Jakovetic, and K. Tserpes, “Fedgreed: A byzantine-robust loss-based aggregation method for federated learning,” in2025 3rd International Conference on Federated Learning Technolo- gies and Applications (FLTA). IEEE, 2025, pp. 348–355

    work page 2025

  35. [35]

    Differ- entially private learning with adaptive clipping,

    G. Andrew, O. Thakkar, B. McMahan, and S. Ramaswamy, “Differ- entially private learning with adaptive clipping,”Advances in Neural Information Processing Systems, vol. 34, pp. 17 455–17 466, 2021

    work page 2021

  36. [36]

    Learning multiple layers of features from tiny images,

    A. Krizhevsky, G. Hintonet al., “Learning multiple layers of features from tiny images,” University of Toronto, Tech. Rep., 2009

    work page 2009

  37. [37]

    Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

    H. Xiao, K. Rasul, and R. V ollgraf, “Fashion-mnist: A novel image dataset for benchmarking machine learning algorithms,”arXiv preprint arXiv:1708.07747, 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  38. [38]

    Mnist handwritten digit database,

    Y . LeCun, “Mnist handwritten digit database,” http://yann.lecun.com/ exdb/mnist/, 2010, aT&T Labs

    work page 2010

  39. [39]

    Flower: A friendly federated learning research framework.arXiv preprint arXiv:2007.14390,

    D. J. Beutel, T. Topal, A. Mathur, X. Qiu, J. Fernandez-Marques, Y . Gao, L. Sani, H. L. Kwing, T. Parcollet, P. P. d. Gusm ˜ao, and N. D. Lane, “Flower: A friendly federated learning research framework,”arXiv preprint arXiv:2007.14390, 2020

    work page arXiv 2007

  40. [40]

    Multi-task federated learning for person- alised deep neural networks in edge computing,

    J. Mills, J. Hu, and G. Min, “Multi-task federated learning for person- alised deep neural networks in edge computing,”IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 3, pp. 630–641, 2021

    work page 2021