  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.
