[2603.21145] NeSy-Edge: Neuro-Symbolic Trustworthy Self-Healing in the Computing Continuum

Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2603.21145 (cs) [Submitted on 22 Mar 2026] Title:NeSy-Edge: Neuro-Symbolic Trustworthy Self-Healing in the Computing Continuum Authors:Peihan Ye, Alfreds Lapkovskis, Alaa Saleh, Qiyang Zhang, Praveen Kumar Donta View a PDF of the paper titled NeSy-Edge: Neuro-Symbolic Trustworthy Self-Healing in the Computing Continuum, by Peihan Ye and 4 other authors View PDF HTML (experimental) Abstract:The computational demands of modern AI services are increasingly shifting execution beyond centralized clouds toward a computing continuum spanning edge and end devices. However, the scale, heterogeneity, and cross-layer dependencies of these environments make resilience difficult to maintain. Existing fault-management methods are often too static, fragmented, or heavy to support timely self-healing, especially under noisy logs and edge resource constraints. To address these limitations, this paper presents NeSy-Edge, a neuro-symbolic framework for trustworthy self-healing in the computing continuum. The framework follows an edge-first design, where a resource-constrained edge node performs local perception and reasoning, while a cloud model is invoked only at the final diagnosis stage. Specifically, NeSy-Edge converts raw runtime logs into structured event representations, builds a prior-constrained sparse symbolic causal graph, and integrates causal evidence with historical troubleshooting knowledge for root-cause an...