The increasing demand for efficient resource allocation in mobile networks has catalyzed the exploration of innovative solutions that could enhance the task of real-time cellular traffic prediction. Under these circumstances, federated learning (FL) stands out as a distributed and privacy-preserving solution to foster collaboration among different sites, thus enabling responsive near-the-edge solutions. In this paper, we comprehensively study the potential benefits of FL in telecommunications through a case study on federated traffic forecasting using real-world data from base stations (BSs) in Barcelona (Spain). Our study encompasses relevant aspects within the federated experience, including model aggregation techniques, outlier management, the impact of individual clients, personalized learning, and the integration of exogenous sources of data. The performed evaluation is based on both prediction accuracy and sustainability, thus showcasing the environmental impact of employed FL algorithms in various settings. The findings from our study highlight FL as a promising and robust solution for mobile traffic prediction, emphasizing its twin merits as a privacy-conscious and environmentally sustainable approach, while also demonstrating its capability to overcome data heterogeneity and ensure high-quality predictions, marking a significant stride towards its integration in mobile traffic management systems.

翻译：移动网络中对高效资源分配日益增长的需求，催生了对能够增强实时蜂窝流量预测任务的创新解决方案的探索。在此背景下，联邦学习（FL）作为一种分布式且保护隐私的解决方案脱颖而出，它能够促进不同站点之间的协作，从而实现响应迅速的近边缘解决方案。本文通过对西班牙巴塞罗那基站（BSs）的真实数据进行联邦流量预测的案例研究，全面探讨了联邦学习在电信领域的潜在优势。我们的研究涵盖了联邦学习实践中的多个相关方面，包括模型聚合技术、异常值处理、个体客户端的影响、个性化学习以及外部数据源的整合。评估基于预测准确性和可持续性两方面进行，从而展示了所采用的联邦学习算法在不同设置下的环境影响。我们的研究结果突显了联邦学习作为一种有前景且稳健的移动流量预测解决方案，强调了其作为隐私意识和环境可持续方法的双重优点，同时也证明了其能够克服数据异质性并确保高质量预测的能力，这标志着其在移动流量管理系统中集成迈出了重要一步。