Secured Scalable Blockchain Networks for Trustworthy Distributed Deep Learning in VANETs
Bibliographic record
Abstract
Distributed deep learning (DDL) within vehicular ad hoc networks (VANETs) holds profound significance in developing smart applications, such as intelligent transport systems and autonomous driving, where multiple parties are coordinated to leverage the training capability and acquisitive intelligence.Blockchain (BC) is a distribution technology promising for trustworthy DDL, holding the divide-and-conquer concept with decentralized management and consensus algorithms to reduce the exposure of sensitive controllers and thus the risks of malicious system-wide attacks.Moreover, zero trust architecture (ZTA) concepts are promoted as innovative cybersecurity solution which can be integrated with BC to address the resource-limited and infrastructure-less issues to augment the strength of DDL in VANETs.In this dissertation, the BC and ZTA potential is explored to construct reliable VANETs for trustworthy data sharing and thus to support significant within-VANET DDL and relevant applications.Firstly, virtualized distributed ledger technology (vDLT) is developed as the multimedia BC platform, followed by vDLT-based VANETs built to solve the unsteady communica-First and foremost, I would like to express my deepest appreaciation to my supervisor, Prof.Richard Yu, who have been working with his wholehearted dedication to supervise my PhD study.His immense knowledge and cutting-edge insights have been encouraging me of elevating my academic research quality.He have also endearvored to find fundings to support my study and daily life to keep my work proceeding smoothly.Without his invaluable advice and continous supports, the achievements during my study would have been impossible.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".