In recent years, wireless networks are evolving complex, which upsurges the use of zero-touch artificial intelligence (AI)-driven network automation within the telecommunication industry. In particular, network slicing, the most promising technology beyond 5G, would embrace AI models to manage the complex communication network. Besides, it is also essential to build the trustworthiness of the AI black boxes in actual deployment when AI makes complex resource management and anomaly detection. Inspired by closed-loop automation and Explainable Artificial intelligence (XAI), we design an Explainable Federated deep learning (FDL) model to predict per-slice RAN dropped traffic probability while jointly considering the sensitivity and explainability-aware metrics as constraints in such non-IID setup. In precise, we quantitatively validate the faithfulness of the explanations via the so-called attribution-based \emph{log-odds metric} that is included as a constraint in the run-time FL optimization task. Simulation results confirm its superiority over an unconstrained integrated-gradient (IG) \emph{post-hoc} FDL baseline.

翻译：近年来，无线网络日趋复杂，这推动了零接触人工智能驱动的网络自动化技术在电信行业的广泛应用。特别是网络切片——5G之后最具前景的技术——将借助人工智能模型来管理复杂的通信网络。此外，当AI执行复杂的资源管理与异常检测时，在实际部署中建立AI黑盒的信任度同样至关重要。受闭环自动化和可解释人工智能的启发，我们设计了一种可解释的联邦深度学习模型，用于预测每切片RAN丢包概率，同时在此非独立同分布场景下将敏感性与可解释性感知指标作为约束条件联合考量。具体而言，我们通过所谓的基于归因的\textit{对数几率指标}定量验证解释的忠实性，并将其作为运行时联邦学习优化任务的约束条件。仿真结果证实，该模型相较于无约束的积分梯度\textit{事后}FDL基线方法具有优越性。