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.

翻译：学校环境中的自闭症谱系障碍诊断系统日益依赖物联网摄像头，然而纯云端处理存在隐私和延迟问题，而纯边缘推理则受限于精度不足。我们提出置信度约束云边知识蒸馏，这是一种分层框架，主要在边缘执行推理，并仅选择性上传低置信度样本至云端。云端生成温度缩放软标签，并通过跨参与学校的全局损失将其蒸馏回边缘模型，从而在不集中原始数据的情况下提升泛化能力。在两个公开的自闭症面部图像数据集上，所提框架取得了87.4\%的优异准确率，证明了其在真实场景中可扩展部署的潜力。