Fed-NTP: A Federated Learning Algorithm for Network Traffic Prediction in VANET
Bibliographic record
Abstract
During the last years, the volume of data produced in smart cities has been growing up, which can cause network traffic. Some of the challenges in an Intelligent Transportation System (ITS) are predicting the network traffic with the highest accuracy, keeping the security of data and being less complex. Artificial Intelligence (AI) algorithms are advantageous solutions to predict, control and avoid network traffic. However, such algorithms brought some costs to the privacy field. Accordingly, besides having an accurate prediction, preserving the privacy of data is an important challenge that should be considered. To cope with this problem, we propose a Federated learning algorithm for Network Traffic Prediction (Fed-NTP) based on Long Short-Term Memory (LSTM) algorithm to train the model locally, which can predict the network traffic flow accurately while preserving privacy. We implement the LSTM algorithm in a decentralized way by using the federate learning (FL) algorithm on the Vehicular Ad-Hoc Network (VANET) dataset and predict network traffic based on the most influential features of network traffic flow in the road and network. Simulation results reveal that the proposed model besides preserving the privacy of data, takes an obvious advantage over other well-known AI algorithms in terms of errors in prediction and the highest$R^{2}-SCORE$(0.975).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".