No Data Left Behind: Federated Learning for Equitable Dermatological Care

Diagnosing skin diseases poses a significant challenge in global healthcare due to the visual complexity of dermatological conditions and stringent data privacy regulations. Convolutional Neural Networks (CNNs) have shown prowess in analyzing dermatological images, but their centralized data reliance raises privacy concerns. This paper introduces a novel framework that integrates Enhanced CNN architectures (EfficientNet-B4 and ResNet-152) with Federated Learning (FL) to achieve accurate and privacy-preserving diagnosis. We present a custom dataset encompassing five prevalent skin diseases (acne, eczema, psoriasis, melanoma, lichen planus) and employ FL methodologies like Federated Averaging (FedAvg) and Federated Proximal (FedProx) to train models collaboratively without sharing raw data. Our approach addresses challenges posed by non-IID data distributions across healthcare institutions while ensuring compliance with GDPR/HL7 regulations. Experimental findings demonstrate superior performance with 95.7% accuracy and 95.3% precision compared to centralized benchmarks. A prototype edge device facilitates real-time and secure diagnosis, effectively bridging the gap between AI-driven healthcare and patient privacy. This study marks a significant stride towards scalable and ethical AI deployment in dermatology.