# TEFL: Turbo Explainable Federated Learning for 6G Trustworthy Zero-Touch Network Slicing

arXiv: https://arxiv.org/abs/2210.10147 · HTML: https://mlsanity.com/paper/2210.10147/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Principles and Practice of Explainable Machine Learning](https://mlsanity.com/paper/2009.11698/) (2020)
- [Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing](https://mlsanity.com/paper/2104.01375/) (2021)
- [Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network](https://mlsanity.com/paper/2210.06649/) (2022)
- [When Machine Learning Meets Wireless Cellular Networks: Deployment, Challenges, and Applications](https://mlsanity.com/paper/1911.03585/) (2019)
- [Towards Ubiquitous AI in 6G with Federated Learning](https://mlsanity.com/paper/2004.13563/) (2020)
- [Federated Orchestration for Network Slicing of Bandwidth and Computational Resource](https://mlsanity.com/paper/2002.02451/) (2020)
- [Knowledge-powered Explainable Artificial Intelligence (XAI) for Network Automation Towards 6G](https://mlsanity.com/paper/2205.05406/) (2022)
- [Internet of Behavior (IoB) and Explainable AI Systems for Influencing IoT Behavior](https://mlsanity.com/paper/2109.07239/) (2021)
- [The Structure of Service Level Agreement of Slice-based 5G Network](https://mlsanity.com/paper/1806.10426/) (2018)

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

- [Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing](https://mlsanity.com/paper/2307.09494/) (2023)

## Abstract (arXiv)

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 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 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 × 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 post-hoc FL baseline.