Explanation-Guided Fair Federated Learning for Transparent 6G RAN
  Slicing
Swastika Roy Hatim Chergui Christos Verikoukis
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.