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.
翻译:移动交通预测在使5G移动网络能够进行智能和高效基础设施规划和管理的道路上非常重要,然而,现有数据仅限于基站记录信息;因此,需要制定培训方法,以产生高质量的预测,能够对不同当事方进行新的观测;传统方法要求从不同的基站收集测量结果,然后将其送往一个中央实体,然后利用收到的数据进行机器学习作业;传播当地观测结果会提高隐私、保密性和性能问题,妨碍机器学习技术的适用性。已经提出了各种分布式学习方法来解决这一问题,但是尚未探讨这些方法对交通预测的应用。在这项工作中,我们研究了用于原始基地站的联邦化学习效果,汇总LTE数据,用于时间序列预测。我们利用5个不同的神经网络结构来评估一步骤预测,这些网络结构经过了培训,以非二元数据为基站的联邦化环境。所提出的算法已经提交给了全球联运预测5G和Feet Front的应用性。我们的结果显示,根据联邦化的计算方法对交通预测结果,对交通预测结果作了相应误差,但还没有加以探讨。我们在这项工作中,我们研究了对原始基站的精确度方法进行评估。