Impact of Aggregation Functions and Learning Settings in Privacy‐Preserving Federated Learning for Skin Cancer Detection
TL;DR
An implementation of federated learning is presented that addresses challenges of collaborative model training among simulated distributed client nodes while ensuring that raw patient data remains confidential by enabling collaborative model training among simulated distributed client nodes while ensuring that raw patient data remains confidential.
Abstract
ABSTRACT Skin cancer is among the most common malignant tumors worldwide, and early detection is essential to improve patient survival and recovery. Conventional AI‐based diagnostic approaches rely on centralized data, which raises security and privacy concerns. This paper presents an implementation of federated learning that addresses these challenges by enabling collaborative model training among simulated distributed client nodes while ensuring that raw patient data remains confidential. A convolutional neural network architecture, ResNet50, is employed at client nodes, with preprocessing steps including image augmentation, contrast enhancement, and lesion segmentation to improve feature extraction on the International Skin Imaging Collaboration (ISIC) dataset. Multiple aggregation algorithms are implemented at the central server, along with both vertical and horizontal federated learning settings. Their performance is evaluated using metrics such as precision, accuracy, recall, and F1‐score. In the horizontal federated learning setting, the FedNova aggregation approach outperformed other methods, achieving an accuracy of 76.45%, F1‐score of 0.73, and a recall of 0.72, demonstrating enhanced overall classification performance among the evaluated aggregation algorithms and stable convergence across clients. These results highlight the impact of federated learning settings and aggregation strategies on overall skin cancer detection performance.