Towards Responsible AI in Autism Care: A Multi-Modal Federated-Edge Framework for Real-Time Behavioral Support

Autism spectrum disorder (ASD) is a condition that is mostly characterized by unpredictable behavioral outbursts that need to be constantly observed and addressed in time. The conventional AI-based clinical decision-support systems (CDSS) have enhanced precision medicine and behavioral health but often do not help to capture the multi-modal nature of the autism behaviors. In addition, the issues relating to data privacy, latency of the system and clinician trust limit their adoption. In this paper, the challenges have been discussed by providing a federated-edge AI framework that coalesces streams of speech, motion, and physiological data into an explainable CDSS. The system, in contrast to the centralized models, utilizes edge intelligence to provide low-latency processing, federated learning with differential privacy to provide secure collaboration, and explainability dashboards to build clinical trust. Assessments of synthetic data show improvements in accuracy, lower latency, and usability, and prove the potential of the model as a scalable, ethical, and transferable model of autism care, behavioral health, and precision medicine.