Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Data

\'Alvaro L\'opez Garc\'ia; Judith S\'ainz-Pardo D\'iaz

REVIEW 4 major objections 6 minor 1 cited by

Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Data

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper introduces FedAvgOpt, a server-side aggregation strategy that reweights each client's model each round by solving a small nonlinear optimization, and reports that it beats five classical aggregation strategies in mean accuracy…

desk verdict A plausible but statistically under-supported heuristic aggregation rule; the empirical ranking could easily be noise, though the method is honest and worth a careful revision, not a desk reject. read the letter →

arxiv 2501.15949 v1 pith:R4PW3A3R submitted 2025-01-27 cs.LG

Judith Sáinz-Pardo Díaz , Álvaro López García This is my paper · ORCID

classification cs.LG

keywords federatedlearningaggregationstrategyFedAvgOptbrainMRIclassificationlimiteddataconvergencemedicalimagingserver-sideoptimization

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 proposes a new federated learning aggregation strategy, FedAvgOpt, and tests it against five classical strategies on a four-client brain MRI classification task with limited training data. The central claim is that FedAvgOpt achieves the highest average test accuracy over ten rounds for all four pretrained CNN backbones considered (VGG16, InceptionV3, ResNet50V2, DenseNet121). The authors interpret this as better and more stable convergence, and note that FedAvgOpt requires no client-side changes beyond sending local model weights. This matters because aggregation is the key privacy-preserving fusion step in federated learning, and improved aggregation could support collaboration between medical centers without sharing imagery.

What carries the argument

The load-bearing object is the per-round weight vector $\alpha \in \mathbb{R}^n$ obtained by minimizing the objective $$f(x) = \sum_{j=1}^n \frac{\|w_{\mathrm{FedAvg}} x - w_j\|^2}{\|w_{\mathrm{FedAvg}} x + w_j\|^2},$$ where $w_{\mathrm{FedAvg}}$ is the data-count-weighted average of the local model weights $w_j$ from the $n$ clients. The aggregated model then becomes $w_{\mathrm{FedAvgOpt}} = \frac{1}{\sum_i n_i} \sum_i n_i w_i \alpha_i$, so that $\alpha = (1,\ldots,1)$ reduces exactly to FedAvg. The server solves this unconstrained nonlinear problem with a standard derivative-free simplex method initialized at the all-ones vector, and the reweighted average is passed to clients as the next round's global model.

What would settle it

Run the same four-client, four-backbone protocol with five or more different random 20/80 split seeds and report whether FedAvgOpt still achieves the highest mean accuracy in every case; if any other strategy surpasses it on average, the claim that it is the most stable across all four backbones would fail. Alternatively, construct a non-i.i.d. split where FedAvgOpt underperforms FedAvg by a clear margin.

Watch

Extended reading notes

Core claim

The central claim is that FedAvgOpt, a server-side aggregation function that reweights client models each round by solving a small optimization problem, outperforms FedAvg, FedAvgM, FedMedian, FedOpt, and FedYogi in mean aggregated test accuracy over ten federated rounds, across all four base models studied. Concretely, Table II reports FedAvgOpt reaching 0.82209 mean accuracy with DenseNet121 versus 0.79740 for FedMedian, and 0.70999 versus 0.67959 with InceptionV3. The paper also reports that FedAvgOpt's per-round accuracy curves are more stable, with fewer sharp drops, and that in three of the four backbones it achieves the highest first-round accuracy, exceeding the others by more than ten percentage points.

Load-bearing premise

The paper assumes that minimizing the ad hoc ratio-sum objective f(x) yields aggregation weights that generalize better, without a theoretical analysis of why that objective relates to generalization; the experiments also rely on a single 20/80 stratified split per client, so the ranking could depend on that particular split.

Editorial extensions

If this is right

Switching the server-side aggregator from FedAvg to FedAvgOpt improves mean accuracy over ten rounds for all four tested backbones in the studied i.i.d. setting, with gains of roughly three accuracy points for InceptionV3 and DenseNet121.
FedAvgOpt requires no client-side modifications and no additional communication beyond the local model weights, so it can be dropped into existing federated learning pipelines.
In three of the four backbones (VGG16, ResNet50V2, DenseNet121), FedAvgOpt achieves the highest first-round accuracy, indicating faster initial convergence.
For an unstable base model such as InceptionV3, FedAvgOpt's per-round curve avoids the significant accuracy drops seen with FedAvg, FedAvgM, FedOpt, and FedYogi.
The method is tested with only 20% of each client's data used for training, so the reported improvements appear specifically in the limited-data regime the paper targets.

Reading between the lines

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

A natural extension is to test FedAvgOpt under non-i.i.d. client distributions, since the current experiments use an i.i.d. stratified split and the paper explicitly lists this as future work; the mechanism may behave differently when client data is heterogeneous.
The proposed objective is an ad hoc ratio of distance terms without a theoretical convergence or generalization guarantee, so a plausible falsifying scenario is a data split or base model where the extra optimization actually degrades accuracy relative to plain FedAvg.
Because the ranking rests on a single random 20/80 split per client with no repeated seeds, an editorial caveat is that the reported order of strategies could be partly an artifact of that particular split; repeated trials would be needed to confirm the ordering.
The ratio structure resembles a normalized distance that may relate to cosine-style similarity between weight vectors, which could offer a bridge to theoretical analysis of FedAvgOpt as a form of robust mean estimation.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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

The new thing here is FedAvgOpt: per-client reweighting factors, alpha, tuned each round by Nelder-Mead against an objective that pulls the aggregated model toward every client model. That is a genuine, if modest, addition to the FedAvg family. The paper also does something right: it compares against five established baselines on four architectures, uses a real medical imaging task, and does not oversell the theory. There is no circularity; alpha is fit on client models, not test data, and the method is not dressed up as something it is not.

Where it falls short is statistics. The main claim — FedAvgOpt is the most stable and best aggregator — rests on one random 20/80 split per client, ten rounds, and no repeated seeds or confidence intervals. For VGG16 the margin over FedAvgM is 0.27 percentage points; for ResNet50V2 it is 0.49 points over FedMedian. With stochastic training and a single split, those gaps are indistinguishable from run-to-run noise. The larger margins on InceptionV3 and DenseNet121 are more suggestive, but without seeds they do not justify the strength of the conclusion. The ad hoc objective (Eq. 2) and the non-normalized weighting in the aggregation formula are also genuine weaknesses, and the notation around w_FedAvg x is sloppy enough to slow a careful reader down.

Still, the central idea is not wrong. It is an incremental heuristic, presented as such in most of the text, and the experiments, while under-powered, are not fabricated. The paper would be substantially improved by adding multiple seeds, reporting standard deviations, testing a non-i.i.d. split, and cleaning up the math. I would not accept it as is, but I would not desk reject it either. It deserves a serious referee and a request for major revision. The right audience is someone working on practical FL aggregation for medical imaging, not someone looking for new theory. I would not cite it in my own work until the empirical claims are backed by proper uncertainty quantification.

Referee Report

4 major / 6 minor

Summary. The paper proposes FedAvgOpt, a server-side federated learning aggregation strategy that reweights each client's contributed model by a per-client scalar alpha. The alphas are obtained by minimizing a heuristic objective f(x) defined in Eq. (1) as the sum, over clients, of ratios of squared distances between a reweighted average model and each local model, using the Nelder-Mead method from the initial point x0 = 1. The method is evaluated on a brain MRI classification task with four clients, four pretrained CNN base models (VGG16, InceptionV3, ResNet50V2, DenseNet121), and five baseline aggregation strategies (FedAvg, FedAvgM, FedMedian, FedOpt, FedYogi), under a simulated limited-data setting with 20% of each client's data used for training. The paper reports mean aggregated test accuracy over ten federated rounds and concludes in Section VII that FedAvgOpt is the most stable aggregation strategy of the six analyzed across the four base models.

Significance. If the empirical claim were firmly established, FedAvgOpt would be a modest but potentially useful contribution: it is a server-side-only aggregation rule that requires no client-side modifications, which is practically attractive for privacy-sensitive medical deployments. The experimental design is transparent in several respects: it uses an open dataset, four architectures with very different parameter counts and depths, six aggregation strategies, and a clearly stated pseudocode for the proposed method. However, the central conclusion currently rests on a single experimental realization with no repeated seeds, no confidence intervals, and no significance testing, and the reported performance metric (mean accuracy over rounds) can favor a strategy that starts well in round 1 even if its final-round performance is not superior. The optimization objective is also introduced heuristically with no theoretical link to generalization or convergence. These issues are load-bearing because the paper's main contribution is its empirical ranking of aggregation strategies; the ranking may change under repeated runs or under a different evaluation metric.

major comments (4)

[Section VI, Table II, Section VII] The central claim that FedAvgOpt is 'the most stable aggregation strategy' is not statistically supported. The experiments appear to use a single random stratified client partition and a single random 20/80 train/test split per client, with no repeated seeds, no confidence intervals, and no significance tests. For two of the four base models the reported margins over the best baseline are very small: VGG16 0.77010 vs. 0.76742 for FedAvgM (+0.27 percentage points) and ResNet50V2 0.79142 vs. 0.78652 for FedMedian (+0.49 percentage points). Training is stochastic and the data split is random, so these differences are within plausible run-to-run variation. The authors should repeat the entire protocol with multiple seeds and report mean and standard deviation (or confidence intervals) for each strategy and model, and ideally perform a paired significance test across seeds.
[Section VI, Table II, Figure 4] The primary reported metric, 'mean aggregated accuracy obtained in the 10 rounds,' is not a standard measure of the quality of the final federated model. Averaging over rounds mixes early-round behavior with convergence behavior, and the paper itself notes that FedAvgOpt starts from a higher first-round accuracy for VGG16, DenseNet121, and ResNet50V2. A strategy that has a high initial accuracy but converges to the same or lower final accuracy can therefore look better under this metric. The authors should additionally report the final-round test accuracy and per-round learning curves with variability, or clearly justify why mean-over-rounds is the appropriate target metric for the claimed convergence improvement.
[Section IV, Eqs. (1)-(2), Algorithm 1] The FedAvgOpt objective is ad hoc: there is no theoretical argument or prior evidence that minimizing the sum of ratios of squared distances between a reweighted average and each local model improves the global model's generalization or convergence. In addition, the alpha_i are optimized without any normalization or non-negativity constraint, so the resulting w_FedAvgOpt is not in general a convex combination of the client models; the paper does not discuss whether negative or large alpha values can occur or what effect they have on the aggregated model. The notation in Eq. (2), 'w_FedAvg x,' is also ambiguous: w_FedAvg appears to be a vector of aggregated parameters, while x is the alpha vector, so the expression should be written as the weighted average (1/sum n_i) * sum_i w_i n_i x_i rather than as a product of w_FedAvg and x. The authors should clarify the formulation, add constraints or justify their absence, and discuss the behavior of the Nelder-Mead optimization from the single starting point x0 = 1.
[Section V, Section V.C] The local training configuration is incompletely specified: the paper gives the optimizer, learning rate, batch size, and number of rounds, but does not state the number of local training epochs (or local steps) per client per round. Local epochs are a critical hyperparameter in federated learning and directly affect the comparison between aggregation strategies. Without this information, the experiments are not fully reproducible, and the observed ranking could depend on an undocumented choice. The authors should report the number of local epochs/steps for each base model and, if varied, justify the choice.

minor comments (6)

[Section I] There is a typo in the Introduction: 'psedudonymization' should be 'pseudonymization.'
[Section VI] There are typos in the results discussion: 'mote than 10%' should be 'more than 10%,' and 'FedAvgOot' should be 'FedAvgOpt.'
[Section V.B] In the ResNet-50 V2 description, '128 sy neurons' appears to be a typo for '128 neurons.'
[Section IV, Eq. (2)] The notation 'w_FedAvg x' in Eq. (2) should be explicitly defined as the weighted average of client weights with coefficients x_i; as written, it looks like a matrix-vector product and is inconsistent with Eq. (1).
[Section V.A] The paper should state the random seed or seeds used for the client partition and the train/test split, since the entire evaluation is based on a single split.
[Section II] The descriptions of FedAvgM, FedMedian, and FedOpt would benefit from explicit references to the exact algorithm numbers and equations in the cited papers, since the current text summarizes rather than reproduces the update rules.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: FedAvgOpt's alpha weights are optimized on client model weights, not on test labels, and the reported accuracy is an independent benchmark result.

full rationale

The FedAvgOpt construction (Section IV, Eqs. 1-2 and Algorithm 1) defines an optimization over the current round's client weights w_i, with the auxiliary objective f(x) measuring a reweighted average's distance to those same local models. The test-set accuracies in Table II are computed after aggregation on held-out client test data and never enter the objective or the Nelder-Mead optimization, so the method is not fitting test outcomes. The only self-citation, reference [10], is used to describe the standard federated learning architecture and is not load-bearing for the proposed strategy's validity or for the convergence comparison. The paper's empirical claim is a benchmark observation rather than a prediction derived from fitted constants, and there is no equation or prior result that forces FedAvgOpt's reported superiority by construction. Statistical concerns about one random split and small margins are robustness/correctness risks, not circularity.

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

The central claim depends mainly on the ad hoc design of the objective function and on the reproducibility of the single-split experiment. No new physical or conceptual entities are introduced.

free parameters (2)

alpha (per-client reweighting factors) = not reported; optimized per round via Nelder-Mead
The method's core is a vector alpha in R^n (n=4 clients) used in w_FedAvgOpt = (sum n_i w_i alpha_i) / (sum n_i). These are free parameters chosen by optimization each round; their optimized values are not reported, so the method's behavior is not transparent.
Nelder-Mead initial point x0 = 1 (vector of ones)
The optimization starts from the FedAvg-equivalent point; this choice may affect the local minimum found, and no sensitivity analysis is provided.

assumptions (4)

ad hoc to paper Minimizing f(x) (Eq. 1) improves the global model's convergence and accuracy
This premise is the basis of FedAvgOpt; no derivation, convergence analysis, or ablation is provided to support it.
domain assumption A single random stratified split into 4 i.i.d. clients with 20% training data is a sufficient testbed
The evaluation uses one split and 10 rounds; results are not repeated with different seeds or data partitions.
domain assumption ImageNet pre-training transfers to brain MRI
All backbones are initialized with ImageNet weights; standard practice but an assumption for this domain.
domain assumption Nelder-Mead finds a meaningful minimum of f
The algorithm is heuristic and no convergence guarantee is given for the nonconvex objective; the final alpha may depend on starting point.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Data." pith.science (2026). https://pith.science/paper/R4PW3A3R

@misc{pith2026250115949,
  author       = {Pith},
  title        = {Pith review of: Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R4PW3A3R}},
  note         = {Machine review of arXiv:2501.15949}
}

read the original abstract

The development of deep learning techniques is a leading field applied to cases in which medical data is used, particularly in cases of image diagnosis. This type of data has privacy and legal restrictions that in many cases prevent it from being processed from central servers. However, in this area collaboration between different research centers, in order to create models as robust as possible, trained with the largest quantity and diversity of data available, is a critical point to be taken into account. In this sense, the application of privacy aware distributed architectures, such as federated learning arises. When applying this type of architecture, the server aggregates the different local models trained with the data of each data owner to build a global model. This point is critical and therefore it is fundamental to analyze different ways of aggregation according to the use case, taking into account the distribution of the clients, the characteristics of the model, etc. In this paper we propose a novel aggregation strategy and we apply it to a use case of cerebral magnetic resonance image classification. In this use case the aggregation function proposed manages to improve the convergence obtained over the rounds of the federated learning process in relation to different aggregation strategies classically implemented and applied.

Figures

Figures reproduced from arXiv: 2501.15949 by the authors.

**Figure 2.** Distribution of the classes in the four clients. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Example of the available images for each of the four categories. Extracted from the data of the first client. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Evolution of the aggregated accuracy in the client’s test set in each round of the FL architecture with each base model. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

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

AI4EOSC: a Federated Cloud Platform for Artificial Intelligence in Scientific Research
cs.DC 2025-12 unverdicted novelty 5.0 of 10

AI4EOSC is a federated cloud platform that integrates modular AI development, serverless AI-as-a-Service, and distributed orchestration with built-in FAIR metadata and provenance tracking for scientific AI workloads in EOSC.

Reference graph

Works this paper leans on

30 extracted references · 14 canonical work pages · cited by 1 Pith paper

[1]

Artificial intelligence in healthcare,

K.-H. Yu, A. L. Beam, and I. S. Kohane, “Artificial intelligence in healthcare,” Nature biomedical engineering, vol. 2, no. 10, pp. 719–731, 2018

work page 2018
[2]

Bohr and K

A. Bohr and K. Memarzadeh, Artificial intelligence in healthcare . Academic Press, 2020

work page 2020
[3]

1–88, May 2016, [Accessed 10-07-2024]

European Commission, “Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/ec (general data protection regulation) (text with eea relevance),” https: //eur-lex.europa.eu...

work page 2016
[4]

——, “Regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and emending certain union legislative acts,” http://data.europa.eu/ eli/reg/2024/1689/oj, [Accessed 10-07-2024]

work page 2024
[5]

Communication-efficient learning of deep networks from decentralized data,

B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics . PMLR, 2017, pp. 1273– 1282

2017
[6]

Impact of age and gender on glioblastoma onset, progression, and management,

A. Colopi, S. Fuda, S. Santi, A. Onorato, V . Cesarini, M. Salvati, C. R. Balistreri, S. Dolci, and E. Guida, “Impact of age and gender on glioblastoma onset, progression, and management,” Mechanisms of ageing and development , vol. 211, p. 111801, 2023

work page 2023
[7]

Magnetic resonance imaging and histopathology of cerebral gliomas,

M. Watanabe, R. Tanaka, and N. Takeda, “Magnetic resonance imaging and histopathology of cerebral gliomas,” Neuroradiology, vol. 34, pp. 463–469, 1992

work page 1992
[8]

Gliomas in adults,

T. Schneider, C. Mawrin, C. Scherlach, M. Skalej, and R. Firsching, “Gliomas in adults,” Deutsches ¨Arzteblatt International, vol. 107, no. 45, p. 799, 2010

work page 2010

Show all 30 references

[9]

Extracellular vesicles in glioblastoma: a challenge and an opportunity,

V . Indira Chandran, S. Gopala, E. H. Venkat, M. Kjolby, and P. Nejsum, “Extracellular vesicles in glioblastoma: a challenge and an opportunity,” NPJ Precision Oncology , vol. 8, no. 1, p. 103, 2024

2024
[10]

Study of the performance and scalability of federated learning for medical imaging with intermittent clients,

J. S ´ainz-Pardo D´ıaz and ´A. L´opez Garc´ıa, “Study of the performance and scalability of federated learning for medical imaging with intermittent clients,” Neurocomputing, vol. 518, pp. 142–154, 2023

2023
[11]

Flower: A friendly federated learning research framework,

D. J. Beutel, T. Topal, A. Mathur, X. Qiu, J. Fernandez-Marques, Y . Gao, L. Sani, K. H. Li, T. Parcollet, P. P. B. de Gusm ˜ao et al. , “Flower: A friendly federated learning research framework,” arXiv preprint arXiv:2007.14390, 2020

2007 arXiv
[12]

Measuring the effects of non- identical data distribution for federated visual classification,

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

1909 arXiv
[13]

Byzantine-robust dis- tributed learning: Towards optimal statistical rates,

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

2018
[14]

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

2003 arXiv
[15]

Federated optimization in heterogeneous networks,

T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V . Smith, “Federated optimization in heterogeneous networks,” Proceedings of Machine learning and systems , vol. 2, pp. 429–450, 2020

2020
[16]

Model aggregation techniques in federated learning: A comprehensive survey,

P. Qi, D. Chiaro, A. Guzzo, M. Ianni, G. Fortino, and F. Piccialli, “Model aggregation techniques in federated learning: A comprehensive survey,” Future Generation Computer Systems , vol. 150, pp. 272–293, 2024

2024
[17]

Model-contrastive federated learning,

Q. Li, B. He, and D. Song, “Model-contrastive federated learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 10 713–10 722

2021
[18]

Scaffold: Stochastic controlled averaging for federated learn- ing,

S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, “Scaffold: Stochastic controlled averaging for federated learn- ing,” in International conference on machine learning . PMLR, 2020, pp. 5132–5143

2020
[19]

Tackling the objective inconsistency problem in heterogeneous federated optimiza- tion,

J. Wang, Q. Liu, H. Liang, G. Joshi, and H. V . Poor, “Tackling the objective inconsistency problem in heterogeneous federated optimiza- tion,” Advances in neural information processing systems , vol. 33, pp. 7611–7623, 2020

2020
[20]

Effectiveness of federated learning and cnn ensemble architectures for identifying brain tumors using mri images,

M. Islam, M. T. Reza, M. Kaosar, and M. Z. Parvez, “Effectiveness of federated learning and cnn ensemble architectures for identifying brain tumors using mri images,” Neural Processing Letters, vol. 55, no. 4, pp. 3779–3809, 2023

2023
[21]

A neuroimaging dataset of brain tumour patients,

C. Pernet, K. Gorgolewski, and W. Ian, “A neuroimaging dataset of brain tumour patients,” 2016

2016
[22]

Distributed federated learning-based deep learning model for privacy mri brain tumor detection,

L. Zhou, M. Wang, and N. Zhou, “Distributed federated learning-based deep learning model for privacy mri brain tumor detection,” arXiv preprint arXiv:2404.10026, 2024

2024 arXiv
[23]

One model to unite them all: Personalized federated learning of multi-contrast mri synthesis,

O. Dalmaz, M. U. Mirza, G. Elmas, M. Ozbey, S. U. Dar, E. Ceyani, K. K. Oguz, S. Avestimehr, and T. C ¸ ukur, “One model to unite them all: Personalized federated learning of multi-contrast mri synthesis,” Medical Image Analysis, vol. 94, p. 103121, 2024

2024
[24]

Specificity- preserving federated learning for mr image reconstruction,

C.-M. Feng, Y . Yan, S. Wang, Y . Xu, L. Shao, and H. Fu, “Specificity- preserving federated learning for mr image reconstruction,” IEEE Trans- actions on Medical Imaging , vol. 42, no. 7, pp. 2010–2021, 2022

2010
[25]

A systematic review on federated learning in medical image analysis,

M. F. Sohan and A. Basalamah, “A systematic review on federated learning in medical image analysis,” IEEE Access, vol. 11, pp. 28 628– 28 644, 2023

2023
[26]

Brain tumor classification (mri),

S. Bhuvaji, A. Kadam, P. Bhumkar, S. Dedge, and S. Kanchan, “Brain tumor classification (mri),” 2020. [Online]. Available: https: //www.kaggle.com/dsv/1183165

2020
[27]

Very deep convolutional networks for large-scale image recognition,

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 , 2014

2014 arXiv
[28]

Rethinking the inception architecture for computer vision,

C. Szegedy, V . Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2818–2826

2016
[29]

Identity mappings in deep residual networks,

K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14. Springer, 2016, pp. 630–645

2016
[30]

Densely connected convolutional networks,

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE confer- ence on computer vision and pattern recognition , 2017, pp. 4700–4708

2017

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

Artificial intelligence in healthcare,

K.-H. Yu, A. L. Beam, and I. S. Kohane, “Artificial intelligence in healthcare,” Nature biomedical engineering, vol. 2, no. 10, pp. 719–731, 2018

work page 2018

[2] [2]

Bohr and K

A. Bohr and K. Memarzadeh, Artificial intelligence in healthcare . Academic Press, 2020

work page 2020

[3] [3]

1–88, May 2016, [Accessed 10-07-2024]

European Commission, “Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/ec (general data protection regulation) (text with eea relevance),” https: //eur-lex.europa.eu...

work page 2016

[4] [4]

——, “Regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and emending certain union legislative acts,” http://data.europa.eu/ eli/reg/2024/1689/oj, [Accessed 10-07-2024]

work page 2024

[5] [5]

Communication-efficient learning of deep networks from decentralized data,

B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics . PMLR, 2017, pp. 1273– 1282

2017

[6] [6]

Impact of age and gender on glioblastoma onset, progression, and management,

A. Colopi, S. Fuda, S. Santi, A. Onorato, V . Cesarini, M. Salvati, C. R. Balistreri, S. Dolci, and E. Guida, “Impact of age and gender on glioblastoma onset, progression, and management,” Mechanisms of ageing and development , vol. 211, p. 111801, 2023

work page 2023

[7] [7]

Magnetic resonance imaging and histopathology of cerebral gliomas,

M. Watanabe, R. Tanaka, and N. Takeda, “Magnetic resonance imaging and histopathology of cerebral gliomas,” Neuroradiology, vol. 34, pp. 463–469, 1992

work page 1992

[8] [8]

Gliomas in adults,

T. Schneider, C. Mawrin, C. Scherlach, M. Skalej, and R. Firsching, “Gliomas in adults,” Deutsches ¨Arzteblatt International, vol. 107, no. 45, p. 799, 2010

work page 2010