Towards Energy-Aware Federated Traffic Prediction for Cellular Networks - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Towards Energy-Aware Federated Traffic Prediction for Cellular Networks

Résumé

Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network design, resource allocation and anomaly mitigation. Although machine learning (ML) is a promising approach to effectively predict network traffic, the centralization of massive data in a single data center raises issues regarding confidentiality, privacy and data transfer demands. To address these challenges, federated learning (FL) emerges as an appealing ML training framework which offers high accurate predictions through parallel distributed computations. However, the environmental impact of these methods is often overlooked, which calls into question their sustainability. In this paper, we address the trade-off between accuracy and energy consumption in FL by proposing a novel sustainability indicator that allows assessing the feasibility of ML models. Then, we comprehensively evaluate state-of-the-art deep learning (DL) architectures in a federated scenario using real-world measurements from base station (BS) sites in the area of Barcelona, Spain. Our findings indicate that larger ML models achieve marginally improved performance but have a significant environmental impact in terms of carbon footprint, which make them impractical for real-world applications.

Dates et versions

hal-04381142 , version 1 (09-01-2024)

Licence

Paternité

Identifiants

Citer

Vasileios Perifanis, Nikolaos Pavlidis, Selim Yilmaz, Francesc Wilhelmi, Elia Guerra, et al.. Towards Energy-Aware Federated Traffic Prediction for Cellular Networks. FMEC 2023 - Eighth International Conference on Fog and Mobile Edge Computing (FMEC), Sep 2023, Tartu, Estonia. pp.93-100, ⟨10.1109/FMEC59375.2023.10306017⟩. ⟨hal-04381142⟩
25 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More