A Confidence-Constrained Cloud-Edge Collaborative Framework for Autism Spectrum Disorder Diagnosis

TL;DR

This paper introduces C3EKD, a hierarchical cloud-edge framework for ASD diagnosis that balances privacy, latency, and accuracy by selectively processing data at the edge and distilling knowledge from the cloud.

Contribution

It presents a novel confidence-constrained knowledge distillation framework that enhances edge model accuracy while preserving privacy and reducing latency.

Findings

Achieved 87.4% accuracy on ASD facial-image datasets.

Effectively balances privacy, latency, and diagnostic accuracy.

Demonstrated scalability for real-world ASD screening applications.

Abstract

Autism Spectrum Disorder (ASD) diagnosis systems in school environments increasingly relies on IoT-enabled cameras, yet pure cloud processing raises privacy and latency concerns while pure edge inference suffers from limited accuracy. We propose Confidence-Constrained Cloud-Edge Knowledge Distillation (C3EKD), a hierarchical framework that performs most inference at the edge and selectively uploads only low-confidence samples to the cloud. The cloud produces temperature-scaled soft labels and distils them back to edge models via a global loss aggregated across participating schools, improving generalization without centralizing raw data. On two public ASD facial-image datasets, the proposed framework achieves a superior accuracy of 87.4\%, demonstrating its potential for scalable deployment in real-world applications.