Federated Convolutional Neural Networks (F-CNNs) for privacy-preserving multi-class skin lesion classification

Skin lesions include a variety of abnormalities found on the skin. These may be benign (not cancerous) or malignant (cancerous). Every year, the number of cases of skin cancer increases globally, increasing the death rate. Medical data is scarce because people are reluctant to provide their health information due to privacy concerns. In this research, a decentralised machine learning approach, Federated learning, is the primary focus of the discipline to preserve patient data. Using this method, models are trained independently on several dispersed devices without sharing the data. To balance the data and enrich the dataset, the Synthetic Minority Over-sampling technique with Edited Nearest Neighbours (SMOTEENN) is used in this study. The HAM10000 dataset was benchmarked using a Convolutional Neural Network (CNN). Seven classes of HAM10000 include vascular skin lesions, benign keratosis, actinic keratosis, melanoma, dermatofibroma, and melanocytic nevi. A centralized method yields an accuracy of 99.39%, and f1-score, precision, and recall of 99.00%. A simulated Federated learning with three clients, ten rounds, and thirty training epochs produced 93.00% precision, 92.00% recall, 92.00% f1-score, and 91.80% accuracy, respectively. At the same time, an increase to four clients and thirty training epochs produced an accuracy, recall, precision, and f1-score of 97.00% with ten rounds.