Estimating Reliability of Workers for Cooperative Distributed Computing
Bibliographic record
Abstract
Internet supercomputing is an approach to solving partitionable, computation-intensive problems by harnessing the power of a vast number of interconnected computers. For the problem of using network supercomputing to perform a large collection of independent tasks, prior work introduced a decentralized approach and provided randomized synchronous algorithms that perform all tasks correctly with high probability, while dealing with misbehaving or crash-prone processors. The main weaknesses of existing algorithms is that they assume either that the average probability of a non-crashed processor returning incorrect results is inferior to 12, or that the probability of returning incorrect results is known to each processor. Here we present a randomized synchronous distributed algorithm that tightly estimates the probability of each processor returning correct results. Starting with the set P of n processors, let F be the set of processors that crash. Our algorithm estimates the probability pi of returning a correct result for each processor i ∈ P - F, making the estimates available to all these processors. The estimation is based on the (ε, δ)-approximation, where each estimated probability p̃iof piobeys the bound Pr[pi(1 - ε) ≤ p̃i≤ pi(1 + ε)] > 1 - δ, for any constants δ > 0 and ε > 0 chosen by the user. An important aspect of this algorithm is that each processor terminates without global coordination. We assess the efficiency of the algorithm in three adversarial models as follows. For the model where the number of non-crashed processors P - F is linearly bounded the time complexity T (n) of the algorithm is O(log n), work complexity W(n) is O(n log n), and message complexity M(n) is O(n log2n). For the model where P - F is bounded by a fractional polynomial we have T(n) = O(n1-alog n log log n), W(n) = O(n log n log log n), and M(n) = O(n log2n log log n). For the model where P - F is bounded by a poly-logarithm we have T(n) = O(n), W(n) = O(n poly log n), and M(n) = O(n log2n poly log n). All bounds are shown to hold with high probability.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".