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<p>Skin diseases pose a major public health challenge, with skin cancer being the most prevalent malignancy. Early detection is critical for improving patient outcomes, yet traditional diagnostic methods rely on expert evaluation, which is limited by clinician variability, image quality, and accessibility constraints. Deep learning (DL)-based models offer automated diagnostic capabilities but require large-scale centralized data aggregation, which conflicts with privacy regulations and poses security risks. Federated Learning (FL) enables collaborative training across multiple hospitals without sharing raw patient data, yet it introduces communication overhead, security vulnerabilities, and interpretability challenges, hindering its real-world deployment. To address these issues, an Encrypted FedAvg-Based Explainable Federated Learning approach has been proposed utilizing a Lightweight Deep Learning Multi-Scale Convolutional Neural Network (LWMS-CNN) for efficient, privacy-enhanced, and interpretable skin cancer diagnosis. The proposed method integrates Homomorphic Encryption (HE) in a simulated federated system for secure model aggregation, privacy enhancements while maintaining strong diagnostic performance. Additionally, SHapley Additive exPlanations (SHAP) and GradCAM enhance interpretability, enabling clinicians to understand AI-driven predictions. Experimental evaluations on the HAM10000 dataset demonstrate that the proposed LWMS-CNN-FL model achieves 98.62% accuracy, with only a 0.3% tradeoff when encryption is applied, ensuring robust security without compromising diagnostic reliability. The model’s generalization ability is further confirmed through assessments on additional benchmark datasets, including ISIC 2019, where it achieved an accuracy of 96.22%, and PAD-UFES-20, where it reached 89.84%. By integrating lightweight deep learning, federated learning, encryption, and explainability, this study presents a scalable, privacy-enhanced, computationally efficient, and clinically interpretable AI-driven solution for secure and accurate early skin cancer detection in world healthcare applications.</p>

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Explainable and secure federated learning for privacy-enhancing skin cancer classification using a lightweight multi-scale CNN

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月28日

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  • Abdullah Siddique Mohammad Sayeed,
  • Shaikh Afnan Birahim,
  • Md Shafiq Ullah,
  • Yamina Islam,
  • Avijit Paul,
  • Mohammad Asif Hasan,
  • Mominul Ahsan,
  • Marcin Kowalski

摘要

Skin diseases pose a major public health challenge, with skin cancer being the most prevalent malignancy. Early detection is critical for improving patient outcomes, yet traditional diagnostic methods rely on expert evaluation, which is limited by clinician variability, image quality, and accessibility constraints. Deep learning (DL)-based models offer automated diagnostic capabilities but require large-scale centralized data aggregation, which conflicts with privacy regulations and poses security risks. Federated Learning (FL) enables collaborative training across multiple hospitals without sharing raw patient data, yet it introduces communication overhead, security vulnerabilities, and interpretability challenges, hindering its real-world deployment. To address these issues, an Encrypted FedAvg-Based Explainable Federated Learning approach has been proposed utilizing a Lightweight Deep Learning Multi-Scale Convolutional Neural Network (LWMS-CNN) for efficient, privacy-enhanced, and interpretable skin cancer diagnosis. The proposed method integrates Homomorphic Encryption (HE) in a simulated federated system for secure model aggregation, privacy enhancements while maintaining strong diagnostic performance. Additionally, SHapley Additive exPlanations (SHAP) and GradCAM enhance interpretability, enabling clinicians to understand AI-driven predictions. Experimental evaluations on the HAM10000 dataset demonstrate that the proposed LWMS-CNN-FL model achieves 98.62% accuracy, with only a 0.3% tradeoff when encryption is applied, ensuring robust security without compromising diagnostic reliability. The model’s generalization ability is further confirmed through assessments on additional benchmark datasets, including ISIC 2019, where it achieved an accuracy of 96.22%, and PAD-UFES-20, where it reached 89.84%. By integrating lightweight deep learning, federated learning, encryption, and explainability, this study presents a scalable, privacy-enhanced, computationally efficient, and clinically interpretable AI-driven solution for secure and accurate early skin cancer detection in world healthcare applications.