Future zero-touch artificial intelligence (AI)-driven 6G network automation requires building trust in the AI black boxes via explainable artificial intelligence (XAI), where it is expected that AI faithfulness would be a quantifiable service-level agreement (SLA) metric along with telecommunications key performance indicators (KPIs). This entails exploiting the XAI outputs to generate transparent and unbiased deep neural networks (DNNs). Motivated by closed-loop (CL) automation and explanation-guided learning (EGL), we design an explanation-guided federated learning (EGFL) scheme to ensure trustworthy predictions by exploiting the model explanation emanating from XAI strategies during the training run time via Jensen-Shannon (JS) divergence. Specifically, we predict per-slice RAN dropped traffic probability to exemplify the proposed concept while respecting fairness goals formulated in terms of the recall metric which is included as a constraint in the optimization task. Finally, the comprehensiveness score is adopted to measure and validate the faithfulness of the explanations quantitatively. Simulation results show that the proposed EGFL-JS scheme has achieved more than $50\%$ increase in terms of comprehensiveness compared to different baselines from the literature, especially the variant EGFL-KL that is based on the Kullback-Leibler Divergence. It has also improved the recall score with more than $25\%$ relatively to unconstrained-EGFL.

翻译：未来零接触人工智能驱动的6G网络自动化需要通过可解释人工智能建立对AI黑盒的信任，其中AI忠实度将与电信关键性能指标共同作为可量化的服务等级协议指标。这要求利用XAI输出生成透明且无偏的深度神经网络。受闭环自动化和解释引导学习的启发，我们设计了一种解释引导式联邦学习方案，通过在训练阶段利用Jensen-Shannon散度捕获XAI策略产生的模型解释，来确保可信预测。具体而言，我们以每切片RAN丢弃流量概率预测为例阐述该概念，同时将以召回率指标表述的公平性目标纳入优化任务约束。最后，采用全面性评分对解释的忠实度进行量化验证。仿真结果表明，与文献中的多种基线方法（尤其是基于Kullback-Leibler散度的EGFL-KL变体）相比，所提EGFL-JS方案在全面性方面提升了50%以上，且相较于无约束EGFL方案，召回率提升超过25%。