Towards Energy-Aware Federated Traffic Prediction for Cellular Networks

TL;DR

This paper evaluates the energy efficiency of federated learning models for cellular traffic prediction, proposing a sustainability indicator to balance accuracy and environmental impact, based on real-world data from Barcelona.

Contribution

It introduces a novel sustainability indicator for federated learning models and assesses the trade-off between accuracy and energy consumption in cellular traffic prediction.

Findings

Larger models offer marginal accuracy improvements

Significant environmental impact from bigger models

Smaller models are more practical for real-world deployment

Abstract

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.