QPoS: Decentralized Stake-Based Leader and Voter Selection in a PBFT System With Mobile Voters
Bibliographic record
Abstract
Both Proof of Stake (PoS) and Delegated Proof of Stake (DPoS) consensus schemes for permissioned blockchains incur the risk of centralization of voting power in the hands of a small number of wealthy voters. In this work, we present Qualified Proof of Stake (QPoS) scheme which alleviates centralization by rewarding truthful behavior of both voters and leaders, and penalizing their untruthful behavior. Leaders are elected according to the current stake which gives preference to more trustworthy nodes. Nodes with low stake at the end of a round which consists of multiple PBFT voting cycles are excluded from voting in subsequent rounds, while nodes with sufficient stake may leave the network temporarily without losing their stake. We consider multiple node classes with different voting behavior and model them using embedded Markov Chain which corresponds to Semi Markov Process (SMP) in order to determine system performance. Our results show the interaction of class populations, voting behavior, and mobility with round size, and show notable stake-based prioritization among the nodes for selection of PBFT leaders. Moreover, we show that higher proportion of well behaved nodes and shorter voting rounds are needed to achieve consensus 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".