Personalized Federated Learning based Joint Latency and Power Optimization for UAV-assisted C-V2X Communications
Bibliographic record
Abstract
This paper investigates the system performance and communication delays between vehicles and unmanned aerial vehicles (UAVs) in UAV-assisted cellular vehicle-to-everything (C-V2X) environment. In order to minimize the weighted total energy consumption of a UAV, we propose to optimize the transmission window length and maximize the UAV’s available transmit power in each transmission window. We also propose to efficiently control the power consumption of the UAV while considering the effects of orthogonal time frequency space modulation. We formulate a multi-objective optimization problem and implement a personalized federated learning (PFL) based solution. The simulation results reveal that with PFL based distributed and co-operative learning, the weighted total energy consumption of the UAV decreases by 10-15% compared with traditional federated learning algorithms such as federated averaging and federated stochastic gradient decent. Moreover, PFL achieves approximately 40% delay reduction in UAV-vehicle communication compared to traditional federated learning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".