Communication-Efficient Distributed Machine Learning: Techniques and Innovations
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Bibliographic record
Abstract
As Artificial Intelligence (AI) technologies continue to advance, the size and complexity of machine learning models are rapidly increasing. Distributed Machine Learning (DML) has been proposed to improve the limitations of centralized training in terms of computational power and memory. However, communication overhead remains a significant obstacle in DML, restricting training efficiency. This paper proposes a hybrid approach combining Adaptive Gradient Compression (AGC) and Locally Updated Stochastic Gradient Descent (LU-SGD) to maintain model performance while reducing communication overhead. Specifically, the communication load is first reduced by compressing the gradient during each transmission round via AGC. Second, this study uses LU-SGD to minimize the number of communication phases by executing several local updates before synchronizing the gradients. Extensive experiments are conducted on Modified National Institute of Standards and Technology (MNIST), Canadian Institute for Advanced Research (CIFAR)-10, and ImageNet datasets with LeNet, Residual Neural Network (ResNet), and Visual Geometry Group (VGG). Experimental results show the hybrid approach reduces communication data while maintaining efficiency and model accuracy. This approach effectively optimizes communication and demonstrates its potential to improve distributed machine learning frameworks' expansion capability and efficiency.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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