Federated Learning for Energy Efficiency in 6G

Abstract

This paper presents a multi-tier Federated Learning (FL) architecture designed to optimize energy efficiency in 6G, with particular emphasis on compliance with the Network Data Analytics Function (NWDAF) standards defined by 3GPP. Unlike existing FL architectures that often overlook energy efficiency and lack full integration with network functions like NWDAF, our proposed architecture integrates AI-driven strategies across multi layers. This multi-tier approach dynamically adjusts computation and communication rounds, reducing energy consumption while maintaining high model accuracy and network performance. By addressing challenges such as data heterogeneity and personalisation through adaptive training, intelligent routing, and advanced model aggregation, the architecture significantly enhances energy efficiency. Initial simulations, aligned with NWDAF processing requirements, underscore the architecture’s suitability for deployment in 6G , offering a scalable, energy-efficient, and privacy-preserving solution that aligns with industry standards and addresses key challenges in distributed learning.