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

arXiv: https://arxiv.org/abs/2307.09494 · HTML: https://mlsanity.com/paper/2307.09494/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 18 Jul 2023.

## Builds on (most cited first, 21 of 21)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations](https://mlsanity.com/paper/1703.03717/) (2017)
- [Tell Me Where to Look: Guided Attention Inference Network](https://mlsanity.com/paper/1802.10171/) (2018)
- [Split Learning over Wireless Networks: Parallel Design and Resource Management](https://mlsanity.com/paper/2204.08119/) (2022)
- [Improving Deep Learning Interpretability by Saliency Guided Training](https://mlsanity.com/paper/2111.14338/) (2021)
- [Explanation-Guided Training for Cross-Domain Few-Shot Classification](https://mlsanity.com/paper/2007.08790/) (2020)
- [Two-Player Games for Efficient Non-Convex Constrained Optimization](https://mlsanity.com/paper/1804.06500/) (2018)
- [Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints](https://mlsanity.com/paper/1807.00028/) (2018)
- [Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning](https://mlsanity.com/paper/2212.03954/) (2022)
- [AI-Native Network Slicing for 6G Networks](https://mlsanity.com/paper/2105.08576/) (2021)
- [Data-driven generation of spatio-temporal routines in human mobility](https://mlsanity.com/paper/1607.05952/) (2016)
- [Machine Guides, Human Supervises: Interactive Learning with Global Explanations](https://mlsanity.com/paper/2009.09723/) (2020)
- [Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision](https://mlsanity.com/paper/2010.03384/) (2020)
- [Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning](https://mlsanity.com/paper/2012.01991/) (2020)
- [On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration](https://mlsanity.com/paper/2211.03088/) (2022)
- [Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network](https://mlsanity.com/paper/2210.06649/) (2022)
- [Explanation-Guided Deep Reinforcement Learning for Trustworthy 6G RAN Slicing](https://mlsanity.com/paper/2303.15000/) (2023)
- [Wireless Network Slicing: Generalized Kelly Mechanism Based Resource Allocation](https://mlsanity.com/paper/1907.02182/) (2019)
- [Quantifying Explainability in NLP and Analyzing Algorithms for Performance-Explainability Tradeoff](https://mlsanity.com/paper/2107.05693/) (2021)
- [TEFL: Turbo Explainable Federated Learning for 6G Trustworthy Zero-Touch Network Slicing](https://mlsanity.com/paper/2210.10147/) (2022)

## Built on by (most cited first, 1 of 1)

- [Federated Learning Based Decentralized Adaptive Intelligent Transmission Protocol for Privacy Preserving 6G Networks](https://mlsanity.com/paper/2512.18432/) (2025)

## Abstract (arXiv)

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.