Fault-Tolerant 1-bit Representation for Distributed Inference Tasks in Wireless IoT
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
In IoT applications, the sensors usually have limited bandwidth and power resources. Therefore, the sensed data should be mapped to a low-bit representation by means of compression and quantization before being transmitted to a central node, called the fusion center (FC). At the FC, a global decision is inferred from this data. In many cases, this data is intended for machine consumption, not for human perception. However, the compression techniques are mainly designed for reconstruction fidelity. The accuracy of the inferred decision at the FC is less considered. In this work, we present an end-to-end framework for learning a 1-bit representation of correlated-sensors data. We also propose a novel loss function and a three-stage training algorithm for learning discriminative binary features at each sensor. Extensive experiments show the proposed framework achieves high compression ratios with a marginal loss in the inferred decision accuracy. Comparatively, the obtained results outperform other benchmark models in the literature.
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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 | 0.000 |
| 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 | 0.000 | 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 it