Remous-Aris Koutsiamanis

Vasileios Perifanis, Nikolaos Pavlidis, Remous-Aris Koutsiamanis et al.

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.

Vasileios Perifanis, Nikolaos Pavlidis, Selim F. Yilmaz et al.

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.

Nikolaos Pavlidis, Vasileios Perifanis, Selim F. Yilmaz et al.

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.