{"ID":2843795,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.06797","arxiv_id":"2511.06797","title":"FedNET: Federated Learning for Proactive Traffic Management and Network Capacity Planning","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 ($R^2 \u003e0.92$), 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.","short_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 distrib...","url_abs":"https://arxiv.org/abs/2511.06797","url_pdf":"https://arxiv.org/pdf/2511.06797v1","authors":"[\"Saroj Kumar Panda\",\"Basabdatta Palit\",\"Sadananda Behera\"]","published":"2025-11-10T07:36:16Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}