Sequential Gradient Coding for Packet-Loss Networks
Bibliographic record
Abstract
We consider distributed computation of a sequence of$J$gradients$\{\mathbf {g}(0), \ldots,\mathbf {g}(J-1)\}$. Each worker node computes a fraction of$\mathbf {g}(t)$in round-$t$and attempts to communicate the result to a master. Master is required to obtain the full gradient$\mathbf {g}(t)$by the end of round-$(t+T)$. The goal here is to finish all the$J$gradient computations, keeping the cumulative processing time as short as possible. Delayed availability of results from individual workers causes bottlenecks in this setting. These delays can be due to factors such as processing delay of workers and packet losses. Gradient coding (GC) framework introduced by Tandonet al.uses coding theoretic techniques to mitigate the effect of delayed responses from workers. In this paper, we primarily target mitigating communication-level delays. In contrast to the classical GC approach which performs coding only across workers ($T=0$), the proposed sequential gradient coding framework is more general, as it allows for coding across workers as well as time. We present a new sequential gradient coding scheme which offers improved resiliency against communication-level delays compared to the GC scheme, without increasing computational load. Our experimental results establish performance improvement offered by the new coding scheme.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".