Private Blockchain and Federated Learning-Based Edge-Iot Platform for Secure Urban Noise Monitoring
Bibliographic record
Abstract
The Internet of Things (IoT) has enabled many services and applications that benefit the urban landscape. One of these services is urban noise monitoring. Noise pollution is an issue that can disrupt the lifestyle and well-being of the populace. An urban noise monitoring system can help analyze the noise levels around the different areas in the city to isolate and address disruptive locations. However, it is a service that heavily relies on its training data. It must remain secure to keep the monitoring system accurate and reliable. So, we propose a secure urban noise monitoring system using a private blockchain and Federated Learning (FL)-based edge-IoT platform. It combines the immutability of blockchain technology with the privacypreserving capabilities of FL, reinforcing the security of training data within an IoT network. Based on our tests, our proposed platform was able to preserve the security, privacy, and integrity of the urban noise monitoring system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".