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

This article presents a comprehensive review of resilient and sustainable federated-edge artificial intelligence (AI) for autism care, integrating multimodal data processing, privacy-preserving technologies, and global ethical standards. It explores how federated learning, edge computing, explainable AI, and differential privacy can support secure and efficient healthcare systems for individuals with autism spectrum disorder (ASD).The article examines the integration of multiple data sources, including speech, behavioral, motion, and physiological information, to support personalized monitoring and clinical decision-making. It also discusses data security, patient privacy, transparency, interoperability, and ethical challenges associated with implementing AI-driven healthcare solutions.The research highlights the potential of federated-edge AI to improve healthcare accessibility, computational efficiency, data protection, and responsible AI adoption in autism care. It contributes to ongoing research at the intersection of artificial intelligence, healthcare informatics, privacy engineering, and sustainable digital health systems.Original publication: World Journal of Advanced Research and Reviews (WJARR), 2025, 28(1), 208–214.DOI: https://doi.org/10.30574/wjarr.2025.28.1.3363