Privacy preserving skin cancer diagnosis through federated deep learning and explainable AI

The classification of human skin disorders, particularly benign and malignant skin cancer, is thoroughly examined in this study with a focus on protecting data privacy. Traditional visual diagnosis of skin disorders is often subjective and complicated by the varying colors, textures, and shapes of lesions. To address these challenges, we propose a privacy-preserving and explainable deep learning (DL) architecture that leverages secure federated learning (FL) on distributed medical data sources without exposing private patient information, ensuring compliance with data protection regulations. Real-world decentralized scenarios are simulated by dividing a skin image dataset into two classes and distributing it among three clients. The Federated Averaging (FedAvg) method is employed to train the VGG19 model-a well-established convolutional neural network (CNN)-over 25 federated communication rounds, after pretraining on ImageNet and fine-tuning for binary classification. To enhance robustness and diversity, dermatology datasets, such as Kaggle, are often used in similar studies for performance evaluation. Additionally, explainable AI (XAI) techniques, such as Gradient-weighted Class Activation Mapping (Grad-CAM), are incorporated to improve transparency and assist clinicians in visualizing and interpreting the model's decision-making process. Experimental results demonstrate that the federated approach maintains data privacy while achieving high classification performance. This work highlights the potential of combining explainability and FL to develop reliable and privacy-conscious AI solutions for dermatological diagnosis.