Edge Selection Non-Cooperative Game in IoT Edge Computing
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
Computational offloading is a pivotal solution to several Internet of Things (IoT) issues as it helps subdue the constrained nature of IoT devices. By harnessing the large capacity at the Edge, IoT devices with limited battery and storage can delegate certain tasks, especially those related to Machine Learning. Because of their restricted capacity, such devices can only store a limited amount of data as a training set for their learning, leading to a faulty prediction with high error rate. To tackle that issue, IoT devices can federate the learning process with other devices while the Edge server acts as an aggregator. However, selecting the appropriate Edge is a significant challenge. In fact, although learning collectively can reduce the prediction error, it also brings about a communication cost that depends on the selected Edge. Thus, in this paper, we propose a Non-Cooperative game where devices autonomously and efficiently select an Edge server in order to reduce both their learning error and communication cost.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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