  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.
