Though Explainable AI (XAI) has made significant advancements, its inclusion in edge and IoT systems is typically ad-hoc and inefficient. Most current methods are "coupled" in such a way that they generate explanations simultaneously with model inferences. As a result, these approaches incur redundant computation, high latency and poor scalability when deployed across heterogeneous sets of edge devices. In this work we propose Explainability-as-a-Service (XaaS), a distributed architecture for treating explainability as a first-class system service (as opposed to a model-specific feature). The key innovation in our proposed XaaS architecture is that it decouples inference from explanation generation allowing edge devices to request, cache and verify explanations subject to resource and latency constraints. To achieve this, we introduce three main innovations: (1) A distributed explanation cache with a semantic similarity based explanation retrieval method which significantly reduces redundant computation; (2) A lightweight verification protocol that ensures the fidelity of both cached and newly generated explanations; and (3) An adaptive explanation engine that chooses explanation methods based upon device capability and user requirement. We evaluated the performance of XaaS on three real-world edgeAI use cases: (i) manufacturing quality control; (ii) autonomous vehicle perception; and (iii) healthcare diagnostics. Experimental results show that XaaS reduces latency by 38% while maintaining high explanation quality across three real-world deployments. Overall, this work enables the deployment of transparent and accountable AI across large scale, heterogeneous IoT systems, and bridges the gap between XAI research and edge-practicality.

翻译：尽管可解释人工智能（XAI）已取得显著进展，但其在边缘计算与物联网系统中的集成通常具有临时性与低效性。现有方法大多采用“耦联”机制——在模型推理的同时生成解释，导致在异构边缘设备集群部署时产生冗余计算、高延迟及可扩展性差等问题。本文提出可解释性即服务（XaaS）——一种将可解释性作为系统级首要服务（而非模型专属特性）的分布式架构。其核心创新在于将推理与解释生成过程解耦，使边缘设备能在资源与延迟约束条件下请求、缓存及验证解释。具体而言，我们引入三项关键技术：（1）基于语义相似性解释检索的分布式解释缓存机制，显著降低冗余计算；（2）确保缓存与新生成解释保真度的轻量级验证协议；（3）根据设备能力与用户需求自适应选择解释方法的动态解释引擎。我们在三个真实边缘AI场景（制造质量控制、自动驾驶感知、医疗诊断）中评估了XaaS性能。实验表明，XaaS在三个部署场景中实现38%的延迟降低，同时保持高解释质量。本研究为大规模异构物联网系统部署透明可问责AI提供了可行方案，弥合了XAI研究与边缘实用性之间的鸿沟。