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

• URL: http://arxiv.org/abs/2503.12435v1
• Date: Sun, 16 Mar 2025 10:14:25 GMT
• Title: XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing
• Authors: Martino Chiarani, Swastika Roy, Christos Verikoukis, Fabrizio Granelli,
• Abstract summary: Network slicing has embraced artificial intelligence (AI) models to manage the growing complexity of communication networks.<n>In such a situation, AI-driven zero-touch network automation should present a high degree of flexibility and viability.<n>This 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.
• Score: 3.3148772440755527
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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