Consensus-based distributed optimization: Practical issues and applications in large-scale machine learning
Bibliographic record
Abstract
This paper discusses practical consensus-based distributed optimization algorithms. In consensus-based optimization algorithms, nodes interleave local gradient descent steps with consensus iterations. Gradient steps drive the solution to a minimizer, while the consensus iterations synchronize the values so that all nodes converge to a network-wide optimum when the objective is convex and separable. The consensus update requires communication. If communication is synchronous and nodes wait to receive one message from each of their neighbors before updating then progress is limited by the slowest node. To be robust to failing or stalling nodes, asynchronous communications should be used. Asynchronous protocols using bi-directional communications cause deadlock, and so one-directional protocols are necessary. However, with one-directional asynchronous protocols it is no longer possible to guarantee the consensus matrix is doubly stochastic. At the same time it is essential that the coordination protocol achieve consensus on the average to avoid biasing the optimization objective. We report on experiments running Push-Sum Distributed Dual Averaging for convex optimization in a MPI cluster. The experiments illustrate the benefits of using asynchronous consensus-based distributed optimization when some nodes are unreliable and may fail or when messages experience time-varying delays.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".