Blockchain-Supported Federated Learning for Trustworthy Vehicular Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The advances in today's IoT devices and machine learning methods have given rise to the concept of Federated Learning. Through such a technique, a plethora of network devices collaboratively train and update a mutual machine learning model while protecting their individual data-sets. Federated learning proves its effectiveness in tackling communication efficiency and privacy-safeguarding issues. Moreover, blockchain was introduced to solve many network issues in regard to data privacy and network single point of failure. In this article, we introduce a solution that integrates both federated learning and blockchain to ensure both data privacy and network security. We present a framework to decentralize the mutual machine learning models on end-devices. A blockchain-based consensus solution as a second line of privacy is used to ensure trustworthy shared training on the fog. The proposed model enables on-end device machine learning without any centralized training of the data nor coordination by utilizing a consensus method in the blockchain. We evaluate and verify our proposed model through simulation to showcase the effectiveness of the adapted scheme in terms of accuracy, energy consumption, and lifetime rate, along with throughput and latency metrics. The proposed model performs with an accuracy rate of ≈ 0.97.
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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 | 0.000 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.013 | 0.029 |
| Research integrity | 0.000 | 0.000 |
| 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 it