A Trustable Federated Learning Framework for Rapid Fire Smoke Detection at the Edge in Smart Home Environments
Why this work is in the frame
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Bibliographic record
Abstract
With the rapid growth of the Internet of Things, sensors have become integral components of smart homes, enabling real-time monitoring and control of various aspects ranging from energy consumption to security. In this context, we cannot underestimate the importance of sensor-based data in ensuring the safety and well-being of occupants, particularly in scenarios involving early detection of fire outbreaks. We propose a novel federated learning (FL) Framework in this study to address the crucial issue of rapid fire smoke detection at the edge of smart home environments. The proposed framework employs three distinct FL algorithms, namely, federated averaging, federated adaptive moment estimation, and federated proximal, for global aggregation of machine learning predictions based on data from various IoT sensors. This framework allows for early prediction by utilizing the computational capabilities at the edge, thereby improving the responsiveness and efficiency of fire safety systems. Furthermore, to improve trust and transparency in the FL framework, explainable artificial intelligence techniques, such as local interpretable model-agnostic explanations (LIMEs) and Shapley additive explanations (SHAP), are integrated. We unveil pivotal features driving predictive outcomes through LIME and SHAP analyses, offering users valuable insights into model decision-making processes.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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