A Federated Transfer Learning-Empowered Blockchain-Enabled Secure Knowledge Sharing Scheme for Unmanned Any Vehicles in Smart Cities
Bibliographic record
Abstract
Smart cities embrace unmanned autonomous vehicles (UxVs) for urban mobility and addressing challenges. UxVs include UAVs, UGVs, USVs, and UUVs, empowered by AI, particularly deep learning (DL), for autonomous missions. However, traditional DL has limitations in adapting to dynamic environments and raises data privacy concerns. Limited data availability and starting from scratch to adapt to a new environment during missions pose challenges. Additionally, cyber threats, particularly in terms of communication and data security, can jeopardize the missions performed by UxVs. This paper proposes a federated transfer learning scheme for UxVs, sharing prior knowledge and training with limited data while ensuring security through blockchain. Domain adaptation with maximum mean discrepancy enhances the DL model's performance in target domains. The proposed scheme's feasibility is demonstrated in an empirical environment, and it outperforms existing works.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".