5 datasets found
  1. f

    Global accuracy with more clients.

    • figshare.com
    xls
    Updated May 15, 2024
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    Muhammad Babar; Basit Qureshi; Anis Koubaa (2024). Global accuracy with more clients. [Dataset]. http://doi.org/10.1371/journal.pone.0302539.t005
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 15, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Muhammad Babar; Basit Qureshi; Anis Koubaa
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with increased data heterogeneity, emphasizing the need for innovative strategies to enhance FL in diverse environments. This research contributes to the practical implementation of FL, extending beyond theoretical concepts and addressing the nuances in medical imaging applications. This research uncovers the inherent challenges in FL due to data diversity. It sets the stage for future advancements in FL strategies to effectively manage data heterogeneity, especially in sensitive fields like healthcare.

  2. f

    Global accuracy with more communiation rounds.

    • figshare.com
    • plos.figshare.com
    xls
    Updated May 15, 2024
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    Muhammad Babar; Basit Qureshi; Anis Koubaa (2024). Global accuracy with more communiation rounds. [Dataset]. http://doi.org/10.1371/journal.pone.0302539.t006
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 15, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Muhammad Babar; Basit Qureshi; Anis Koubaa
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with increased data heterogeneity, emphasizing the need for innovative strategies to enhance FL in diverse environments. This research contributes to the practical implementation of FL, extending beyond theoretical concepts and addressing the nuances in medical imaging applications. This research uncovers the inherent challenges in FL due to data diversity. It sets the stage for future advancements in FL strategies to effectively manage data heterogeneity, especially in sensitive fields like healthcare.

  3. f

    Datasets detail.

    • plos.figshare.com
    xls
    Updated May 15, 2024
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    Muhammad Babar; Basit Qureshi; Anis Koubaa (2024). Datasets detail. [Dataset]. http://doi.org/10.1371/journal.pone.0302539.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 15, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Muhammad Babar; Basit Qureshi; Anis Koubaa
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with increased data heterogeneity, emphasizing the need for innovative strategies to enhance FL in diverse environments. This research contributes to the practical implementation of FL, extending beyond theoretical concepts and addressing the nuances in medical imaging applications. This research uncovers the inherent challenges in FL due to data diversity. It sets the stage for future advancements in FL strategies to effectively manage data heterogeneity, especially in sensitive fields like healthcare.

  4. f

    Global accuracy using Dataset-III.

    • plos.figshare.com
    xls
    Updated May 15, 2024
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    Muhammad Babar; Basit Qureshi; Anis Koubaa (2024). Global accuracy using Dataset-III. [Dataset]. http://doi.org/10.1371/journal.pone.0302539.t008
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 15, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Muhammad Babar; Basit Qureshi; Anis Koubaa
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with increased data heterogeneity, emphasizing the need for innovative strategies to enhance FL in diverse environments. This research contributes to the practical implementation of FL, extending beyond theoretical concepts and addressing the nuances in medical imaging applications. This research uncovers the inherent challenges in FL due to data diversity. It sets the stage for future advancements in FL strategies to effectively manage data heterogeneity, especially in sensitive fields like healthcare.

  5. Global accuracy using MLP.

    • plos.figshare.com
    xls
    Updated May 15, 2024
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    Muhammad Babar; Basit Qureshi; Anis Koubaa (2024). Global accuracy using MLP. [Dataset]. http://doi.org/10.1371/journal.pone.0302539.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 15, 2024
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Muhammad Babar; Basit Qureshi; Anis Koubaa
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with increased data heterogeneity, emphasizing the need for innovative strategies to enhance FL in diverse environments. This research contributes to the practical implementation of FL, extending beyond theoretical concepts and addressing the nuances in medical imaging applications. This research uncovers the inherent challenges in FL due to data diversity. It sets the stage for future advancements in FL strategies to effectively manage data heterogeneity, especially in sensitive fields like healthcare.

  6. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Muhammad Babar; Basit Qureshi; Anis Koubaa (2024). Global accuracy with more clients. [Dataset]. http://doi.org/10.1371/journal.pone.0302539.t005

Global accuracy with more clients.

Related Article
Explore at:
xlsAvailable download formats
Dataset updated
May 15, 2024
Dataset provided by
PLOS ONE
Authors
Muhammad Babar; Basit Qureshi; Anis Koubaa
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with increased data heterogeneity, emphasizing the need for innovative strategies to enhance FL in diverse environments. This research contributes to the practical implementation of FL, extending beyond theoretical concepts and addressing the nuances in medical imaging applications. This research uncovers the inherent challenges in FL due to data diversity. It sets the stage for future advancements in FL strategies to effectively manage data heterogeneity, especially in sensitive fields like healthcare.

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