Mobile traffic prediction is of great importance on the path of enabling 5G
mobile networks to perform smart 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 on different parties are in demand.
Traditional approaches require collecting measurements from different base
stations and sending them to a central entity, followed by performing machine
learning operations using the received data. The dissemination of local
observations raises privacy, confidentiality, and performance concerns,
hindering the applicability of machine learning techniques. Various distributed
learning methods have been proposed to address this issue, but their
application to traffic prediction has yet to be explored. In this work, we
study the effectiveness of federated learning applied to raw base station
aggregated LTE data for time-series forecasting. We evaluate one-step
predictions using 5 different neural network architectures trained with a
federated setting on non-iid data. The presented algorithms have been submitted
to the Global Federated Traffic Prediction for 5G and Beyond Challenge. Our
results show that the learning architectures adapted to the federated setting
achieve equivalent prediction error to the centralized setting, pre-processing
techniques on base stations lead to higher forecasting accuracy, while
state-of-the-art aggregators do not outperform simple approaches.

本文研究联邦学习应用于基站聚合 LTE 数据的时序预测的有效性。我们使用 5 种不同的神经网络架构在联邦设置下进行一步预测，结果表明：适应联邦设置的学习架构与集中式设置具有相当的预测误差，基站的预处理技术导致更高的预测准确性，而现代聚合器并不比简单的方法好。