Consensus-based distributed optimization: Practical issues and applications in large-scale machine learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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