Edge-Federated CNN Framework for Distributed Acne Severity Classification Across Devices
Keywords:
Convolutional neural network, Dermatology, Edge artificial intelligence, Federated learning, MobileNetV3, Privacy-preserving machine learning, Smartphone imagingAbstract
Privacy, device constraints, and data heterogeneity affect acne severity assessment from facial photographs. This study presents a privacy-oriented edge-federated framework using MobileNetV3 for four acne-severity categories: mild, moderate, severe, and very severe. The reported dataset contains 25,000 facial images, including a 5,000-image held-out test set, with 224×224 input resolution and approximately 100 clients per round over 200 communication rounds. The reported 2.1 MB model achieved sub-second inference across four tested device classes. Federated training uses local optimization with server-side weighted aggregation, while privacy protection incorporates update noise and secure aggregation. The reported communication volume decreased from 450 GB to 100 GB (a 77.8% reduction), while the target performance was reached in 154 versus 200 communication rounds. Several experimental details require further verification, including data partitioning, preprocessing, privacy parameters, attack methodology, confusion-matrix counts, and baseline metrics. The framework is positioned as an automated decision-support approach rather than a substitute for clinical assessment.
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