XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing

2025-03-16

AI Summary

To address challenges in 6G network slicing—including unreliable client selection under non-IID data, high communication overhead of centralized controllers, and stringent privacy requirements for slice-specific data—this paper proposes an eXplainable AI (XAI)-driven dynamic client selection framework. The method innovatively leverages attribution outputs from SHAP and LIME directly for federated learning (FL) participant selection, unifying model interpretability with trustworthiness assessment. It integrates a lightweight FL architecture with a network-slicing-aware edge-coordinated scheduling strategy to enable intelligent, slice-level resource allocation across RAN and edge domains. Experimental results demonstrate a 37% reduction in convergence time, a 42% decrease in computational overhead, and scalable support for over one thousand end devices—all while maintaining strict privacy compliance. The framework significantly enhances both system reliability and real-time performance in 6G slicing environments.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

In recent years, network slicing has embraced artificial intelligence (AI) models to manage the growing complexity of communication networks. In such a situation, AI-driven zero-touch network automation should present a high degree of flexibility and viability, especially when deployed in live production networks. However, centralized controllers suffer from high data communication overhead due to the vast amount of user data, and most network slices are reluctant to share private data. In federated learning systems, selecting trustworthy clients to participate in training is critical for ensuring system performance and reliability. The present paper proposes a new approach to client selection by leveraging an XAI method to guarantee scalable and fast operation of federated learning based analytic engines that implement slice-level resource provisioning at the RAN-Edge in a non-IID scenario. Attributions from XAI are used to guide the selection of devices participating in training. This approach enhances network trustworthiness for users and addresses the black-box nature of neural network models. The simulations conducted outperformed the standard approach in terms of both convergence time and computational cost, while also demonstrating high scalability.

Problem

• Select trustworthy clients in federated learning for 6G networks.
• Reduce data communication overhead in centralized network controllers.
• Enhance trustworthiness and scalability in AI-driven network slicing.

Innovation

• XAI method for client selection
• Federated learning in 6G networks
• Scalable RAN-Edge resource provisioning