Asynchronous Federated Based Vehicular Edge Computation Offloading
Bibliographic record
Abstract
The expansion of vehicle-to-everything(V2X) systems has grown substantially in recent years due to technological breakthroughs like vehicle edge computing (VEC) and 5G. The rapidly developing field of VEC transfers computationally demanding activities to a nearby VEC server, allowing real-time applications for vehicles. Conventional deep reinforcement learning (DRL) algorithms are inadequate for addressing privacy concerns when outsourcing sensitive data activities. This work introduces an asynchronous federated deep reinforcement learning (AFDRL) approach for task offloading techniques to maximize computation rate while ensuring queue stability and data privacy. To tackle the issue, our research examines computation and queue models for executing tasks at the vehicle or roadside unit (RSU). We introduce a Lyapunov-enhanced deep reinforcement learning approach to address the optimization issue. The outcomes of the simulation show that our proposed approach may significantly improve the computation rate and maintain queue stability in the context of task outsourcing issues, as compared to several baseline methods.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".