Adaptive Sparsification for Communication-Efficient Distributed Learning
Bibliographic record
Abstract
This work addresses the trade-off between convergence and the overall delay in heterogeneous distributed learning systems, where the devices encounter diverse and dynamic communication conditions. We propose to apply adaptive sparsification across the devices and over iterations, formulating an optimization problem to minimize the overall delay while ensuring a specified level of convergence. The resultant stochastic optimization problem cannot be handled by conventional Lyapunov optimization techniques due to the dependency of the per-iteration objective function on the previous iterations. To overcome this challenge, we propose AdaSparse, an online algorithm with a novel per-slot problem that can be solved optimally by searching over a finite discrete space. We further introduce a low-complexity approximation of AdaSparse, termed LC-AdaSparse, which features linear computational complexity and diminishing approximation error. We show that AdaSparse offers strong performance guarantees, simultaneously achieving sub-linear dynamic regret in terms of delay and the optimal rate in terms of convergence. Numerical experiments on classification tasks using standard datasets and various models demonstrate that our approach effectively reduces the communication delay compared with existing benchmarks, to achieve the same levels of learning accuracy.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".