Sixth-generation (6G) networks anticipate intelligently supporting a massive number of coexisting and heterogeneous slices associated with various vertical use cases. Such a context urges the adoption of artificial intelligence (AI)-driven zero-touch management and orchestration (MANO) of the end-to-end (E2E) slices under stringent service level agreements (SLAs). Specifically, the trustworthiness of the AI black-boxes in real deployment can be achieved by explainable AI (XAI) tools to build transparency between the interacting actors in the slicing ecosystem, such as tenants, infrastructure providers and operators. Inspired by the turbo principle, this paper presents a novel iterative explainable federated learning (FL) approach where a constrained resource allocation model and an \emph{explainer} exchange -- in a closed loop (CL) fashion -- soft attributions of the features as well as inference predictions to achieve a transparent and SLA-aware zero-touch service management (ZSM) of 6G network slices at RAN-Edge setup under non-independent identically distributed (non-IID) datasets. In particular, we quantitatively validate the faithfulness of the explanations via the so-called attribution-based \emph{confidence metric} that is included as a constraint in the run-time FL optimization task. In this respect, Integrated-Gradient (IG) as well as Input $\times$ Gradient and SHAP are used to generate the attributions for the turbo explainable FL (TEFL), wherefore simulation results under different methods confirm its superiority over an unconstrained Integrated-Gradient \emph{post-hoc} FL baseline.

翻译：第六代（6G）网络预计将智能支持大量与各类垂直用例共存的异构切片。这一背景迫切要求采用人工智能（AI）驱动的零接触管理编排（MANO），在严格的服务水平协议（SLA）下实现端到端（E2E）切片。具体而言，现实部署中AI黑盒的可信度可通过可解释人工智能（XAI）工具实现，从而在切片生态系统中构建交互参与者（如租户、基础设施提供商和运营商）之间的透明度。受涡轮原理启发，本文提出一种新型迭代可解释联邦学习（FL）方法，其中约束资源分配模型与“解释器”以闭环（CL）方式交换特征的软归因及推理预测，从而在非独立同分布（non-IID）数据集下，于RAN-Edge环境中实现6G网络切片的透明且SLA感知的零接触服务管理（ZSM）。特别地，我们通过所谓的基于归因的“置信度度量”定量验证解释的忠实性，并将其作为约束条件纳入运行时FL优化任务。为此，采用Integrated-Gradient（IG）、Input × Gradient与SHAP生成涡轮可解释FL（TEFL）的归因，不同方法下的仿真结果证实其优于无约束的Integrated-Gradient事后FL基线。