Reliable Federated Learning in Vehicular Communication Networks: An Intelligent Vehicle Selection and Resource Optimization Scheme
Bibliographic record
Abstract
In this paper, we propose a reliable federated learning (FL) scheme for vehicular communication networks. The scheme is named intelligent vehicle selection and resource optimization (IVSRO), which aims to improve the federate learning reliability by reducing the probability of incorrect packet transmission in mobility scenario, and determining the most suitable vehicle for learning based on the incorrect packet probability. Specifically, we introduce a FL model for the vehicular communication network and analyze the probability of incorrect packet transmission caused by dynamic channel changes under this network. In consideration of FL convergence accuracy, an optimization problem is formulated to minimize the incorrect packet transmission rate, which is achieved through selecting the optimal connected vehicles from the training set, allocating transmission power and wireless spectrum resources to the selected vehicles. By employing convergence analysis and determining the optimal power for each selected vehicle, the proposed optimization problem can be handled using a bipartite matching algorithm. Simulation results show that the identification accuracy of the proposed IVSRO scheme is higher than that of existing baseline schemes. The results of this study demonstrate how the proposed IVSRO scheme improve the reliability of the FL scheme in vehicular communication networks while considering the varying channel conditions and proper vehicle selection, making it valuable for FL implementations in the domains of intelligent transportation and road safety management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".