Achilles: Efficient TEE-Assisted BFT Consensus via Rollback Resilient Recovery
Bibliographic record
Abstract
BFT consensus that uses Trusted Execution Environments (TEEs) to improve the system tolerance and performance is gaining popularity. However, existing works suffer from TEE rollback issues, resulting in a tolerance-performance tradeoff. In this paper, we propose Achilles, an efficient TEE-assisted BFT protocol that breaks the tradeoff. The key idea behind Achilles is removing the expensive rollback prevention of TEEs from the critical path of committing transactions. To this end, Achilles adopts a rollback resilient recovery mechanism, which allows nodes to assist each other in recovering their states. Besides, Achilles follows the chaining spirit in modern chained BFT protocols and leverages customized chained commit rules to achieve linear message complexity, end-to-end transaction latency of four communication steps, and fault tolerance for the minority of Byzantine nodes. Achilles is the first TEE-assisted BFT protocol in line with CFT protocols in these metrics. We implement a prototype of Achilles based on Intel SGX and evaluate it in both LAN and WAN, showcasing its outperforming performance compared to several state-of-the-art counterparts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".