In the context of sixth-generation (6G) networks, where diverse network slices coexist, the adoption of AI-driven zero-touch management and orchestration (MANO) becomes crucial. However, ensuring the trustworthiness of AI black-boxes in real deployments is challenging. Explainable AI (XAI) tools can play a vital role in establishing transparency among the stakeholders in the slicing ecosystem. But there is a trade-off between AI performance and explainability, posing a dilemma for trustworthy 6G network slicing because the stakeholders require both highly performing AI models for efficient resource allocation and explainable decision-making to ensure fairness, accountability, and compliance. To balance this trade off and inspired by the closed loop automation and XAI methodologies, this paper presents a novel explanation-guided in-hoc federated learning (FL) approach where a constrained resource allocation model and an explainer exchange -- in a closed loop (CL) fashion -- soft attributions of the features as well as inference predictions to achieve a transparent 6G network slicing resource management in a 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 confidence metric that is included as a constraint to guide the overall training process 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 our proposed in-hoc scheme, wherefore simulation results under different methods confirm its success in tackling the performance-explainability trade-off and its superiority over the unconstrained Integrated-Gradient post-hoc FL baseline.

翻译：在第六代（6G）网络中，多样化的网络切片共存，采用人工智能驱动的零接触管理与编排变得至关重要。然而，在真实部署中确保人工智能黑箱的可信性充满挑战。可解释人工智能工具能够在切片生态系统的利益相关者之间建立透明度方面发挥关键作用。但人工智能性能与可解释性之间存在权衡，这给可信的6G网络切片带来了困境，因为利益相关者既需要高性能的人工智能模型以实现高效资源分配，也需要可解释的决策来确保公平性、问责性和合规性。为平衡这一权衡，受闭环自动化和可解释人工智能方法的启发，本文提出了一种新颖的基于解释的注入式联邦学习方法，其中约束资源分配模型与解释器以闭环方式交换特征的软归因及推理预测，从而在非独立同分布数据集下，于无线接入网-边缘部署中实现透明的6G网络切片资源管理。具体而言，我们通过所谓的基于归因的置信度度量对解释的真实性进行定量验证，该度量作为约束条件，在运行时联邦学习优化任务中指导整体训练过程。在此方面，本文采用积分梯度以及输入乘以梯度和SHAP方法，为我们提出的注入式方案生成归因。跨不同方法的仿真结果证实了该方案在应对性能-可解释性权衡方面的成功，并验证了其相较于无约束的积分梯度事后联邦学习基线的优越性。