Workload Allocation for Distributed Coded Machine Learning: From Offline Model-Based to Online Model-Free
Why this work is in the frame
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Bibliographic record
Abstract
Distributed machine learning (ML) is an important Internet-of-Things (IoT) application. In traditional partitioned learning (PL) paradigm, a coordinator divides a high-dimensional dataset into subsets, which are processed on IoT devices. The execution time of PL can be seriously bottlenecked by slow devices named stragglers. To mitigate the negative impact of stragglers, distributed coded machine learning (DCML) was recently proposed to inject redundancy into the subsets using coding techniques. With this redundancy, the coordinator no longer requires the processing results from all devices, but only from a subgroup, where stragglers can be eliminated. This article aims to bring the burgeoning field of DCML to the wider community. After outlining the principles of DCML, we focus on its workload allocation, which addresses the appropriate level of injected redundancy to minimize the overall execution time. We highlight the fundamental trade-off and point out two critical design choices in workload allocation: model-based versus model-free, and offline versus online. Despite the predominance of offline model-based approaches in the literature, online model-based approaches also have a wide array of use case scenarios, but remain largely unexplored. At the end of the article, we propose the first online model-free workload allocation scheme for DCML, and identify future paths and opportunities along this direction.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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