Federated Averaging Optimization for Efficient Skin Cancer Image Analysis

A skin cancer diagnosis has greatly improved with machine learning (ML), but data privacy and computational e ffi ciency issues still exist. With a primary focus on Federated Averaging (FedAvg), this study presents an optimal approach to Federated Learning (FL) to increase e ffi ciency and accuracy for skin cancer picture processing. FedAvg uses diverse datasets to train institutional mod- els jointly without exchanging sensitive data, protecting patient privacy. In this work, we present an in-depth analysis of FedAvg’s performance over the datasets of skin cancer images, outperforming conventional approaches in terms of diagnostic accuracy while requiring less communication overhead. The findings show that FL can revolutionize medical diagnosis by providing a safe, pre- cise, and e ff ective framework for model training across numerous institutions. This work has improved the ability to diagnose skin cancer and paved the way for future FL systems that take privacy and high computing e ffi ciency into account for the analysis of medical images.