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.

翻译：蜂窝网络流量预测是第五代（5G）及未来网络优化中的关键活动，因为精准的预测对于智能网络设计、资源分配及异常缓解至关重要。尽管机器学习（ML）是有效预测网络流量的可行方法，但在单一数据中心集中处理海量数据会引发保密性、隐私性及数据传输需求等问题。为应对这些挑战，联邦学习（FL）作为一种极具吸引力的ML训练框架应运而生，通过并行分布式计算提供高精度预测。然而，这些方法的环境影响常被忽视，从而对其可持续性提出质疑。本文通过提出一种新型可持续性指标来评估ML模型的可行性，进而研究FL中精度与能耗之间的平衡。在此基础上，我们利用西班牙巴塞罗那地区基站（BS）站点的真实测量数据，全面评估了联邦场景下最先进的深度学习（DL）架构。研究结果表明，更大的ML模型虽能带来微乎其微的性能提升，但其在碳足迹方面的环境影响显著，这使得它们在实际应用中缺乏可行性。