Co-operative Edge Intelligence for C-V2X Communication using Federated Reinforcement Learning
Why this work is in the frame
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Bibliographic record
Abstract
This paper examines the application of federated reinforcement learning (FRL) to enable resource-constrained vehicular edge nodes to learn their communication parameters from a central parameter server (PS). In cellular vehicle-to-everything communication (C-V2X), non independently-and-identically-distributed (non-i.i.d.) data samples impose additional communication requirements and increase the training time for model convergence. By exploring correlations between local model updates and the global model aggregation distributions, we accelerate this convergence using FRL. In the proposed method, Q-values undergo weight adaptation at each training round to update the global model. Local gradient vectors at vehicles and global gradient vectors at the PS measure the contribution of vehicle local models. Furthermore, the Q-values are quantified via nonlinear mapping that reinforces positive rewards, leading to dynamic measurements of local model contributions. Using FRL, policy-based and value-based learning methods reduce the number of communication rounds by upto 40%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it