Cross Layer Optimization Using AI/ML Assisted Federated Edge Learning in 6G Networks Article Swipe

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Spiros Louvros , A. K. Pandey , Yashesh Buch ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.20944/preprints202511.0751.v1

This paper introduces a novel methodology that integrates 6G wireless Federated Edge Learning (FEEL) frameworks with optimization strategies spanning from the MAC layer to the Physical layer. In the context of mobile edge computing, ensuring robust channel estimation within the 6G network slicing paradigm presents critical challenges, particularly in managing data retransmissions. Inaccurate updates from distributed 6G devices can undermine the reliability of Federated Learning (FL), affecting its overall performance. To address this, we propose an AI/ML assisted algorithm for global optimization in FL-based 6G networks, where the decision-making process leverages radial basis functions (RBF) to assess options based on learned preferences rather than relying on direct evaluations of the objective function.

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Type: article
Landing Page: https://doi.org/10.20944/preprints202511.0751.v1
PDF: https://www.preprints.org/frontend/manuscript/5c7a0a0a45fd90216c6e9584f9de124e/download_pub
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DOI: https://doi.org/10.20944/preprints202511.0751.v1

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Title: Cross Layer Optimization Using AI/ML Assisted Federated Edge Learning in 6G Networks

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Publication year: 2025

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Publication date: 2025-11-11

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Authors: Spiros Louvros, A. K. Pandey, Yashesh Buch

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Landing page: https://doi.org/10.20944/preprints202511.0751.v1

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PDF URL: https://www.preprints.org/frontend/manuscript/5c7a0a0a45fd90216c6e9584f9de124e/download_pub

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