# Federated Learning for 5G Base Station Traffic Forecasting

arXiv: https://arxiv.org/abs/2211.15220 · HTML: https://mlsanity.com/paper/2211.15220/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 58 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 29 of 29)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [WeatherBench: A benchmark dataset for data-driven weather forecasting](https://mlsanity.com/paper/2002.00469/) (2020)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach](https://mlsanity.com/paper/2003.08725/) (2020)
- [Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach](https://mlsanity.com/paper/2007.09712/) (2020)
- [An Experimental Review on Deep Learning Architectures for Time Series Forecasting](https://mlsanity.com/paper/2103.12057/) (2021)
- [Federated Learning for Malware Detection in IoT Devices](https://mlsanity.com/paper/2104.09994/) (2021)
- [Electrical Load Forecasting Using Edge Computing and Federated Learning](https://mlsanity.com/paper/2201.11248/) (2022)
- [Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting](https://mlsanity.com/paper/2101.10460/) (2021)
- [Federated Neural Collaborative Filtering](https://mlsanity.com/paper/2106.04405/) (2021)
- [Data Minimization for GDPR Compliance in Machine Learning Models](https://mlsanity.com/paper/2008.04113/) (2020)
- [The Cost of Training Machine Learning Models over Distributed Data Sources](https://mlsanity.com/paper/2209.07124/) (2022)
- [Federated Learning for Short-term Residential Load Forecasting](https://mlsanity.com/paper/2105.13325/) (2021)
- [Sequential Models in the Synthetic Data Vault](https://mlsanity.com/paper/2207.14406/) (2022)
- [Mobile Traffic Classification through Physical Channel Fingerprinting: a Deep Learning Approach](https://mlsanity.com/paper/1910.11617/) (2019)
- [Forecasting Busy-Hour Downlink Traffic in Cellular Networks](https://mlsanity.com/paper/2207.01373/) (2022)

## Built on by (most cited first, 15 of 15)

- [Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach](https://mlsanity.com/paper/2404.03702/) (2024)
- [Towards Energy-Aware Federated Traffic Prediction for Cellular Networks](https://mlsanity.com/paper/2309.10645/) (2023)
- [Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting](https://mlsanity.com/paper/2412.04081/) (2024)
- [Federated Learning for Early Dropout Prediction on Healthy Ageing Applications](https://mlsanity.com/paper/2309.04311/) (2023)
- [Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting](https://mlsanity.com/paper/2502.09744/) (2025)
- [Optimal Look-back Horizon for Time Series Forecasting in Federated Learning](https://mlsanity.com/paper/2511.12791/) (2025)
- [GraFSTNet: Graph-based Frequency SpatioTemporal Network for Cellular Traffic Prediction](https://mlsanity.com/paper/2602.13282/) (2026)
- [Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning](https://mlsanity.com/paper/2310.14456/) (2023)
- [Distributed Learning for Wi-Fi AP Load Prediction](https://mlsanity.com/paper/2405.05140/) (2024)
- [Intelligent Client Selection for Federated Learning using Cellular Automata](https://mlsanity.com/paper/2310.00627/) (2023)
- [LOGSAFE: Logic-Guided Verification for Trustworthy Federated Time-Series Learning](https://mlsanity.com/paper/2411.03231/) (2024)
- [Evaluation of Bio-Inspired Models under Different Learning Settings For Energy Efficiency in Network Traffic Prediction](https://mlsanity.com/paper/2412.17565/) (2024)
- [D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data](https://mlsanity.com/paper/2507.11471/) (2025)
- [Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgrids](https://mlsanity.com/paper/2508.08022/) (2025)
- [F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting](https://mlsanity.com/paper/2608.09082/) (2026)

## Abstract (arXiv)

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.