CRS: A Privacy-Preserving Two-Layered Distributed Machine Learning Framework for IoV
Bibliographic record
Abstract
Nowadays, vehicles can provide many valuable data (such as the videos recorded by dashcams) for analytical model building. Integrating vehicular ad hoc networks with the Internet of Things (IoT), the Internet of Vehicles (IoV) has a promising future. In IoV, vehicles maintain their own communication, computing, and learning capabilities. Thus, instead of sending the data to a central server for model training, which leads to a high communication overhead, vehicles can train the data locally. However, it is still a challenge to preserve the privacy while keeping both the communication and computation overheads of vehicles acceptable. In this article, we present a distributed machine learning framework with a two-layered architecture. The architecture uniquely involves vehicle clusters, roadside units, and a central server, which provides a basic guarantee to the vehicle privacy and also limits the overhead. By carefully adopting cryptographic tools and techniques, the framework has the following properties: 1) it preserves the privacy of the local inputs and model weight vectors to all parties; 2) it protects the identities and trajectories of vehicles; 3) packet loss is handled in the application layer; 4) the evaluation shows that it is lightweight for vehicles. Compared with other existing works, the proposed framework is more suitable for IoV.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".