PrestigeBFT: Revolutionizing View Changes in BFT Consensus Algorithms with Reputation Mechanisms
Bibliographic record
Abstract
Passive view-change protocols are widely employed in BFT algorithms; however, they present the risks of selecting unavailable or slow servers as leaders. To tackle these challenges, we propose PrestigeBFT, a novel BFT consensus algorithm that incorporates an active view-change protocol with reputation mechanisms. PrestigeBFT evaluates a server's reputation based on its past behavior and elects more reputable servers as leaders. Our reputation mechanism incentivizes protocol-abiding behavior while penalizing faulty servers by imposing computational work. PrestigeBFT significantly enhances system availability and efficiency by avoiding unavailable or slow servers being assigned as leaders. Under normal operation, PrestigeBFT achieves$5\times$higher throughput than the baseline that uses passive view-change protocols. In addition, PrestigeBFT's throughput remains unaffected under benign faults and witnesses only a 24% drop under a variety of Byzantine faults, whereas the baseline throughput drops by 62% and 69%, respectively. In the long run, while the baseline's availability struggles at 37%, PrestigeBFT progressively improves its availability to over 90%.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".