Federated Deep Learning and Real-Time Inference on Edge Computing Device for Skin Cancer Classification

Skin cancer is one of the most common and dangerous cancers, with global mortality rates continuing to increase each year. Alongside rapid advancements in Artificial Intelligence (AI) within the medical field, significant challenges have emerged, particularly related to patient data privacy and security. In response to these challenges, this research aims to develop a skin cancer classification system that not only ensures the security of patient data but also maintains model efficiency on devices with limited computing power by Federated Learning and real-time inference on edge computing platforms. The proposed approach combines deep learning through an Xception-based Convolutional Neural Network (CNN) architecture with Federated Learning. Federated Learning enables decentralized model training by utilizing a global server and multiple local servers, where sensitive data remain on local nodes and only model updates are shared for aggregation. The experiments were conducted using two benchmark datasets, HAM10000 (10,000 images) and ISIC 2019 (25,331 images). The resulting global federated model achieved an accuracy of 98.8%. In addition to training evaluation, the proposed model was further assessed during the inference stage on edge devices to evaluate its real-world performance under limited computational resources. Performance benchmarking was conducted on NVIDIA Jetson Orin and Raspberry Pi platforms, where Raspberry Pi 5 demonstrated the fastest inference time of 0.16 s.