Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing

• URL: http://arxiv.org/abs/2307.09494v1
• Date: Tue, 18 Jul 2023 15:50:47 GMT
• Title: Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing
• Authors: Swastika Roy, Hatim Chergui, Christos Verikoukis
• Abstract summary: We design an explanation-guided federated learning (EGFL) scheme to ensure trustworthy predictions. Specifically, we predict per-slice RAN dropped traffic probability to exemplify the proposed concept. It has also improved the recall score with more than $25%$ relatively to unconstrained-EGFL.
• Score: 0.5156484100374059
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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