Resilient and Sustainable Federated-Edge AI for Autism Care: Integrating Multi-Modal Data, Privacy, and Global Ethical Standards

The issue of autism spectrum disorder (ASD) is one of the problems that need to be recognized in time and addressed. Although AI-based clinical decision-support systems (CDSS) have improved precision medicine, behavioral health and workforce planning [1-3], there is limited use in autism care. The main obstacles are the impossibility to integrate multi-modes, patient privacy threat, and clinician trust deficit. This paper presents a robust federated-edge AI system, which integrates speech, motion and physiological surveillance into an explanation explainable CDSS. The framework can be used to enhance the technical performance and ethical accountability through the leveraging of federated learning, with differential privacy, edge intelligence (low-latency responsiveness), and workforce-aware explainability dashboards. The findings on synthetic data show improvements in accuracy, responsiveness, and usability of the systems by clinicians. Outside the autism care, the model offers a long-term and transferable base of behavioral health and precision medicine, along with international ethical principles of AI.