Computation Offloading for Edge Intelligence in Two-Tier Heterogeneous Networks
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
Drivenby the increasing need of massive data analysis and the rising concern about data privacy, implementing machine learning (ML) at network edge is drawing increasing attention, where local users are expected to process massive raw data without sharing data to a remote central server. However, due to the limited computing power of user equipments, how to deal with the rich data is a critical problem for each user. Based on computation offloading and edge learning, we propose an edge intelligence (EI) learning framework in two-tier heterogeneous networks to alleviate the computing pressure of users. Focusing on the minimum time delay of model training, we analyze the completion time of local learning in parallel manner and obtain the optimal offloading ratio in the proposed EI framework. Aiming at the strict interference constraint of the macrocell base station (MBS), a priority-based power allocation algorithm is designed. The analysis and simulation results verify the proposed algorithm can improve the data transmission rate and reduce the task completion time while satisfying the interference constraints of the MBS and maximum tolerable delay of learning tasks. In addition, the partial computation offloading can effectively improve the learning accuracy within a given learning time budget.
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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 | 0.003 |
| 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