PrestigeBFT: Revolutionizing View Changes in BFT Consensus Algorithms with Reputation Mechanisms
Why this work is in the frame
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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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5\times$</tex> 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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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