Cross Layer Optimization Using AI/ML-Assisted Federated Edge Learning in 6G Networks
Abstract
This paper introduces a novel methodology that integrates 6G wireless Federated Edge Learning (FEEL) frameworks with MAC to PHY cross layer optimization strategies. In the context of mobile edge computing, typically ensuring robust channel estimation within the 6G network use cases presents critical challenges, particularly in managing data retransmissions. Inaccurate updates from distributed 6G devices can undermine the reliability of federated learning, affecting its overall performance. To address this, rather than relying on direct evaluations of the objective function, we propose an AI/ML-assisted algorithm for global optimization based on radial basis functions (RBFs) decision-making process to assess learned preference options.
Fields of Study & Subjects
Privacy-Preserving Technologies in Data
Advanced Wireless Communication Technologies
Advanced MIMO Systems Optimization
Publication details
- Journal / source
- Future Internet
- Publisher
- MDPI AG
- Volume
- 18
- Issue
- 2
- Pages
- 71–71
- Publication date
- 30 January 2026
- Language
- EN