Federated Learning Platform for Secure Object Recognition in Connected and Autonomous Vehicles
Bibliographic record
Abstract
Integrating smart technologies in vehicles has brought rise to connected and autonomous vehicles (CAVs), One of the services impacted by this paradigm shift is driving assistance. Most systems use learning-based approaches such as object recognition to improve transportation quality. So, these services require exchanging and sharing data along the CAV network, which raises security issues. Within this work is a federated learning (FL)-based platform as a step towards secure CAV systems. It uses FL to secure client data locally and alleviate pressure on CAV servers and services against targeted attacks. A testbed evaluates the platform's feasibility in preserving the integrity of its implemented classifier while keeping a level of security of its training data. According to experimental results, the FL-based implementation can maintain the integrity of the classifier's accuracy even after introducing its distributive scheme. Also, security evaluations present the benefits of FL reinforcing network security and improving client data privacy. Based on the results, the proposed platform proves its feasibility as an integrity-preserving and secure option for object recognition-based driving assistance services in CAVs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".