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

arXiv: https://arxiv.org/abs/2511.06797 · HTML: https://mlsanity.com/paper/2511.06797/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 10 of 10)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Character-Aware Neural Language Models](https://mlsanity.com/paper/1508.06615/) (2015)
- [Concept Drift Detection in Federated Networked Systems](https://mlsanity.com/paper/2109.06088/) (2021)
- [Survey on Machine Learning for Traffic-Driven Service Provisioning in Optical Networks](https://mlsanity.com/paper/2209.05080/) (2022)
- [Federated Learning over Next-Generation Ethernet Passive Optical Networks](https://mlsanity.com/paper/2109.14593/) (2021)
- [Federated Learning Approach for Lifetime Prediction of Semiconductor Lasers](https://mlsanity.com/paper/2203.12414/) (2022)
- [Scalable Federated Learning over Passive Optical Networks](https://mlsanity.com/paper/2010.15454/) (2020)
- [A Fair Federated Learning Framework for Collaborative Network Traffic Prediction and Resource Allocation](https://mlsanity.com/paper/2502.06743/) (2025)

## Abstract (arXiv)

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 R2 score. Results indicate that FL achieves accuracy close to centralized training, with shorter prediction horizons consistently yielding the highest accuracy (R2 >0.92), while longer horizons providing meaningful forecasts (R2 ≈ 0.45--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.