A Hierarchical Blockchain-Enabled Secure Aggregation Algorithm for Federated Learning in IoV
Bibliographic record
Abstract
Federated learning (FL), as a distributed machine learning paradigm, facilitates collaborative training without sharing raw data and holds promise for effective application in the Internet of Vehicles (IoV) for tasks, such as traffic flow prediction and driving behavior analysis. However, the efficiency of FL systems relies on the integrity of the local dataset and the level of user contribution. Vulnerabilities to attacks by malicious users and suboptimal aggregation methods can compromise system performance. To address these issues, this article proposes a blockchain-based FL secure aggregation algorithm to bolster FL robustness. Specifically, in the absence of a centralized trust authority in the IoV, we establish a hierarchical blockchain-empowered IoV reputation management framework that leverages smart contracts to create a trustworthy environment for reputation sharing. Additionally, a lightweight consensus protocol tailored for blockchain efficiency is proposed, thus facilitating a flexible and effective implementation of FL in the IoV. Furthermore, we introduce a reputation-based model selection evaluation scheme and, based on this, a robust FL secure aggregation algorithm. This novel reputation assessment strategy mitigates the effects of interaction uncertainties and integrates a broader spectrum of IoV-specific reputation determinants, thereby enhancing the precision of model selection. The simulation results validate the proposed framework’s superiority in terms of robustness, adaptability, and security.
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How this classification was reachedexpand
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".