Decentralized Data Allocation via Local Benchmarking for Parallelized Mobile Edge Learning
Bibliographic record
Abstract
Multi-Access Edge Computing (MEC) has emerged as a computing paradigm that can facilitate the use of Mobile Edge Learning (MEL), where Machine Learning (ML) models are processed at the edge. In MEL, it is important to address system heterogeneity in a way that minimizes staleness to improve learning accuracy. To do so, a centralized data allocation approach is typically used. However, this approach tends to overlook the privacy of learners, since learners' capabilities are assumed to be known beforehand by the orchestrator. In this context, we propose the Data Allocation via Benchmarking (DAB) scheme. DAB is a decentralized data allocation scheme that eliminates staleness and achieves a certain QoS while preserving the privacy of learners. DAB does not allow any information about the learners to be known to the orchestrator. Instead, each learner estimates the upper bound on the amount of data that it can train such that a certain training deadline is not exceeded. In addition, DAB proposes a novel method to enable each learner to accurately estimate its own hardware characteristics via benchmarking. Extensive performance evaluations on a real testing environment have shown that DAB can outperform the centralized data allocation scheme by up to 12% and 26% in terms of loss and prediction accuracy, respectively. Performance evaluations also show that the proposed benchmarking scheme yields an 83% reduction in benchmarking error compared to a prominent baseline scheme.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".