Related papers: FedNET: Federated Learning for Proactive Traffic Management and Network Capacity Planning

FedNET: Federated Learning for Proactive Traffic Management and Network Capacity Planning

URL: http://arxiv.org/abs/2511.06797v1
Date: Mon, 10 Nov 2025 07:36:16 GMT
Title: FedNET: Federated Learning for Proactive Traffic Management and Network Capacity Planning
Authors: Saroj Kumar Panda, Basabdatta Palit, Sadananda Behera,
Abstract summary: FedNET is a framework for early identification of high-risk links in large-scale communication networks.<n>FedNET employs Federated Learning to model the temporal evolution of node-level traffic in a distributed manner.
Score: 1.9818048023246968
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We propose FedNET, a proactive and privacy-preserving framework for early identification of high-risk links in large-scale communication networks, that leverages a distributed multi-step traffic forecasting method. FedNET employs Federated Learning (FL) to model the temporal evolution of node-level traffic in a distributed manner, enabling accurate multi-step-ahead predictions (e.g., several hours to days) without exposing sensitive network data. Using these node-level forecasts and known routing information, FedNET estimates the future link-level utilization by aggregating traffic contributions across all source-destination pairs. The links are then ranked according to the predicted load intensity and temporal variability, providing an early warning signal for potential high-risk links. We compare the federated traffic prediction of FedNET against a centralized multi-step learning baseline and then systematically analyze the impact of history and prediction window sizes on forecast accuracy using the $R^2$ score. Results indicate that FL achieves accuracy close to centralized training, with shorter prediction horizons consistently yielding the highest accuracy ($R^2 >0.92$), while longer horizons providing meaningful forecasts ($R^2 \approx 0.45\text{--}0.55$). We further validate the efficacy of the FedNET framework in predicting network utilization on a realistic network topology and demonstrate that it consistently identifies high-risk links well in advance (i.e., three days ahead) of the critical stress states emerging, making it a practical tool for anticipatory traffic engineering and capacity planning.

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