  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.
