Vehicular Blockchain-Based Collective Learning for Connected and Autonomous Vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The accuracy of the ML model is essential for the further development of AI-enabled CAVs. With the increasing complexity of on-board sensor systems, the large amount of raw data available for learning can however cause big communication burdens and data security issues. To alleviate the communication cost yet improve the accuracy of machine learning with preserved data privacy is an important issue to address in CAVs. In this article, we survey the existing literature toward efficient and secured learning in a dynamic wireless environment. In particular, a BCL framework for AI-enabled CAVs is presented. The framework enables distributed CAVs to train ML models locally and upload to blockchain network to overall utilize the "collective intelligence" of CAVs while avoiding large amounts of data transmission. Blockchain is then applied to protect the distributed learned models. We evaluate the performance of the presented framework by simulations and discuss a range of open research issues that need to be addressed in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.002 | 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