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August 22, 2025Advanced Sciences and Technology Journal.

Enhancing Lung Cancer Detection with Advanced Federated Learning Aggregation Techniques

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Authors

KSkarim shamekhAKArabi KeshkMSMohamed Sakr

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Overview

Advanced aggregation methods boost accuracy in lung cancer detection while ensuring data privacy and security.

Key Points

  • FedMA aggregation method achieved the highest accuracy of 99.28%, surpassing other strategies in performance.
  • The study utilized federated learning techniques to train models on decentralized medical imaging datasets while preserving patient privacy.
  • This research adopts a unique combination of KNN classifier with FL methods to tackle challenges posed by non-IID data.
  • Incorporating advanced aggregation techniques can enhance the robustness and adaptability of AI in healthcare applications.

Cite This Study

shamekh et al. (2025) studied this question.

synapsesocial.com/papers/68af5f13ad7bf08b1eae2008https://doi.org/10.21608/astj.2025.389024.1063
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Privacy-Preserving Federated Learning for Multi-Institutional Lung Cancer Severity Detection2026 · 2 citations
  2. 2Federated Learning for Breast Cancer Classification: A Comparative Study of Aggregation Methods2026
  3. 3Advanced Federated Learning Techniques for Multi-Institutional Lung Disease Detection utilizing Chest Radiographs and Computed Tomography Scans2026
  4. 4Federated Learning and Optimization for Lung Cancer Detection Using FedResNet Model2026
  5. 5A Collaborative Federated Learning Framework for Lung and Colon Cancer Classifications2024 · 9 citations