A Distributed GAN-Based Framework for Low-Latency and Efficient Learning in the Internet of Vehicles
Bibliographic record
Abstract
Generative Adversarial Networks (GANs) have shown promise in enabling intelligent and privacy-preserving data synthesis within decentralized environments like the Internet of Vehicles (IoV). However, applying GANs in such dynamic, resource-constrained, and latency-sensitive settings poses significant challenges, particularly due to intermittent connectivity, mobility-induced disruptions, and communication overhead. In this paper, we propose a novel collaborative Multi-Discriminator GAN (MD-GAN) approach tailored for IoV, where Road Side Units (RSUs) act as generators and vehicles function as mobile discriminators providing distributed feedback. Unlike traditional centralized or synchronous GAN setups, our proposed approach leverages an early update strategy that allows generators to proceed once a minimum threshold of feedback is received, along with a feedback selection mechanism that considers only discriminators offering improved loss scores. We adopt the Wasserstein GAN (WGAN) formulation to ensure stable convergence under sparse and asynchronous feedback conditions. The proposed approach is implemented through a hybrid simulation architecture combining NS3 and PyTorch to model both networklevel interactions and learning dynamics. Experimental results demonstrate that the proposed approach significantly reduces training latency and network overhead compared to the baselines, while maintaining competitive generator accuracy. Our findings highlight the effectiveness of communication-efficient GAN training in highly dynamic vehicular networks.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".