  Cellular traffic prediction is of great importance on the path of enabling 5G
mobile networks to perform intelligent and efficient infrastructure planning
and management. However, available data are limited to base station logging
information. Hence, training methods for generating high-quality predictions
that can generalize to new observations across diverse parties are in demand.
Traditional approaches require collecting measurements from multiple base
stations, transmitting them to a central entity and conducting machine learning
operations using the acquire data. The dissemination of local observations
raises concerns regarding confidentiality and performance, which impede the
applicability of machine learning techniques. Although various distributed
learning methods have been proposed to address this issue, their application to
traffic prediction remains highly unexplored. In this work, we investigate the
efficacy of federated learning applied to raw base station LTE data for
time-series forecasting. We evaluate one-step predictions using five different
neural network architectures trained with a federated setting on
non-identically distributed data. Our results show that the learning
architectures adapted to the federated setting yield equivalent prediction
error to the centralized setting. In addition, preprocessing techniques on base
stations enhance forecasting accuracy, while advanced federated aggregators do
not surpass simpler approaches. Simulations considering the environmental
impact suggest that federated learning holds the potential for reducing carbon
emissions and energy consumption. Finally, we consider a large-scale scenario
with synthetic data and demonstrate that federated learning reduces the
computational and communication costs compared to centralized settings.
