A Novel Adaptive Gradient Compression Scheme: Reducing the Communication Overhead for Distributed Deep Learning in the Internet of Things
Why this work is in the frame
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Bibliographic record
Abstract
Distributed deep learning deployed in an edge computing environment is a promising approach for extracting accurate information from raw sensor data from Internet of Things (IoT). But the distributed training suffers from heavy communication overheads between a master node and multiple compute nodes due to frequent transmission of gradients, which limits the training efficiency of the distributed deep learning. In this article, we propose a novel algorithm named ProbComp-LPAC (ProbComp: probability compression and LPAC: layer parameters adaptive compression), which can reduce the communication overhead and improve the training efficiency of the distributed deep learning. ProbComp-LPAC adopts a probability equation to select the gradients and uses different compression rates in different layers of deep neural networks. Comparing with other methods, such as adaptive compression (AdaComp) and lazily aggregated quantized compression (LAQ), the performance of ProbComp-LPAC is not only faster in the training speed but also higher in the accuracy of the test.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it