Multi-Agent Transformer-Based Workload Allocation and Worker Selection in Distributed Coded Machine Learning
Bibliographic record
Abstract
Machine learning (ML) has been used in a wide range of applications. In recent years, the growing complexity of ML models has led to substantial computational demands. Distributed Machine Learning (DML) frameworks address this challenge by parallelizing computation across multiple devices. Yet, DML systems often suffer from the straggler effect, where the slowest devices delay task completion. Distributed Coded Machine Learning (DCML) mitigates this issue by injecting redundancy into subtasks, allowing partial results to suffice for completing the overall task. To fully exploit DCML, two critical problems, worker selection and workload allocation, must be addressed effectively, especially in large-scale edge networks characterized by unstable connectivity, dynamic device availability, and high-dimensional decision spaces. In this work, we propose a Multi-Agent Transformer-based workload Allocation and worker Selection (MAT-AS) scheme for DCML in large-scale edge networks. Technically, MAT-AS converts the multi-agent joint policy optimization problem into a sequential process to make robust decisions on worker selection and workload allocation. Experimental results demonstrate that MAT-AS significantly outperforms baseline approaches in task completion time and cost efficiency, making it a promising solution for efficient and adaptive DCML. Specifically, MAT-AS reduces task completion time by approximately 12% and computation cost by approximately 15% compared to the best baseline method.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".