Split Learning-Based Robust Resource Allocation for Consumer Electronics in Smart Cities
Why this work is in the frame
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Bibliographic record
Abstract
In the smart city, high-density deployment of consumer electronics (CE) may lead to mutual interference, resulting in imperfect estimation of the channel state information (CSI). To tackle the problem, this paper proposes a split learning-based robust resource allocation for CEs in smart cities. We constructed an interference hypergraph model and divided resource allocation conflicts in overlapping areas into multiple virtual sub-cells (VSCs) to reduce the impact of mutual interference for the CSI. Then, we take into account the imperfect CSI and design a robust optimization model to maximize the throughput of the network in the VSCs. Due to the imperfections of CSI and the introduction of random channel parameters, solving robust optimization models is challenging. Hence, we propose the split robust learning algorithm based on interference hypergraph (SRLA-IH), which utilizes split learning theory to learn models and obtain more accurate uncertainty sets, effectively reducing the problems caused by imperfect CSI in smart cities. Numerical results demonstrate that compared with other algorithms, our proposed algorithm can achieve excellent network throughput and improve resource allocation utilization even under imperfect CSI.
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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 | 0.001 | 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 | 0.001 |
| 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