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Cited by 42Privacy-Preserving Technologies in DataMulti-Criteria Decision MakingArtificial Intelligence in Healthcare

Fuzzy Consensus With Federated Learning Method in Medical Systems

Open access
Poap, Dawid·2021·IEEE Access
DOI 10.1109/access.2021.3125799
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Abstract

Large-scale group decision-making (LSGDM) is one of the main open problems where a decision is made by many different results. Moreover, there is also a problem with how to make the decision when there is no all information. This uncertainty can be very problematic for many different solutions in artificial intelligence. In this paper, we propose to extend a federated learning (FL) approach to not only a training process but also for making a decision using many different classifiers. This solution is applied in LSGDM, where many different results are intended for the classification of various data and can be used for deciding, even when some of the data are missing. For this purpose, we propose a fuzzy consensus that can be used in these problems. The contribution of this paper is the new way of using FL and extending its operation to many different classifiers. Our proposition was described for medical purposes and evaluated to show the advantages of the proposal. The proposal obtained 89,12% of accuracy on HAM10000, which is one of the best results compared to state-of-art.

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39 of 39 references verified0 citation errors · 0 not checkable

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  • Polap and their team published three research papers [17], [19], [22], all focused on skin cancer detection with FL.

    “Researcher Polap and their team have published three research papers [17], [19], [22], all of them focused on skin cancer detection with the FL environment.”

    From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [22]

  • Six reviewed papers [17], [19], [22], [23], [29], [30] used more than 10,000 images individually.

    “Finally, we found six papers [17], [19], [22], [23], [29], [30] all of them have used more than 10,000 images individually.”

    From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [22]

  • Połap and the groups used the MNIST: HAM10000 dataset in a series of articles [17], [19], [22] with FL environment.

    “Połap and the groups used the dataset in a series of articles [17], [19], [22] with FL environment.”

    From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [22]

  • Inception was used in [17], [19], and [22]; AlexNet in [17] and [19].

    “Other pre-trained CNN models are Inception ( [17], [19], [22]), AlexNet ( [17], [19]).”

    From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [22]

  • In [22], the proposed VGG-based FL model achieved 89.82% accuracy for classifying images as disease and not disease.

    “In [22], the author classified the images as diseases and not a disease, their proposed VGG based FL model achieved 89.82% accuracy.”

    From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [22]

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Related

Authors
Poap, D.
ORCID
Poap, Dawid
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021
Volume
9
Pages
150383-150392
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2021.3125799
OpenAlex
W3213480973
Cited by
42
References
42
Citations by year
2026: 2 · 2025: 4 · 2024: 14 · 2023: 13 · 2022: 9
FWCI
3.18
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Silesian University of Technology (BKM-512/RMS2/2021, 09/020/RGJ21/0008)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Multi-Criteria Decision Making
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
federated learning, large-scale group decision-making, ensemble classifiers, missing data, medical image classification, HAM10000, skin lesion classification

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