Federated Learning for Skin Cancer Classification

Cancer is one of the deadliest diseases globally. Early detection is crucial for effective treatment. This paper proposes a new method that adopts federated learning for skin cancer classification. Experimental results demonstrate the potential of the proposed method for enhancing the accuracy of skin cancer classification. The proposed method can achieve 98% accuracy on the HAM10000 [1] dataset.