Federated Learning for Melanoma Classification : Analysing Diverse Federated Approaches

Abstract

Federated learning has emerged as a revolutionary method for training machine learning models across disparate data sources. This method ensures that data privacy and security are maintained during the training process, which is especially important in sensitive industries such as healthcare. This review article presents a comprehensive investigation of the use of federated learning strategies to the categorization of melanoma. It investigates a variety of methodologies and the effectiveness of these approaches in utilizing distributed datasets. In this article, a number of different federated learning frameworks, such as FedAvg, FedProx, and customized federated learning techniques, are evaluated, along with their applications in dermatological image analysis. Important factors such as the accuracy of the model, the effectiveness of communication, the management of heterogeneity in data, and the protection of privacy are being examined. This paper highlighted the promise of federated learning to revolutionize melanoma classification. Federated learning has the ability to enable collaborative model training without compromising the security of patient data. The purpose of this work is to provide academics and practitioners who are interested in improving melanoma detection by federated learning with significant insights and future directions. These insights are provided by synthesising previous accomplishments and highlighting present difficulties.