SecurShard: A Model for Hierarchical Fault Detection in Blockchain Sharding
Bibliographic record
Abstract
Sharding increases concurrency in a blockchain system by splitting validators into groups (shards). Each shard processes a different transaction block such that throughput increases linearly in proportion to the number of shards. Contemporary sharding systems assume that shards are ‘perfect’. Therefore, shards need to be formed in such a way that they have negligible probability of failure. However, there are several limitations. In reality, shards can be faulty (violates the ‘perfect’ shard assumption) and an invalid block validated by a faulty shard cannot be detected until the block is appended to the blockchain. Thus, there is no fault detection mechanism during transaction processing. In this paper, we present a hierarchical fault detection model called SecurShard. The SecurShard model proposes mechanisms of combining shards into groups, one-to-all mapping of a transaction block to all shards in a group, and 100% consensus requirement to validate and append a block to blockchain; all these to ensure that a potentially invalid block is detected with high probability during transaction processing rather than after appending to blockchain. Furthermore, the mapping scheme relaxes the constraint that all shards need to be ‘perfect’. This also leads to the possible usage of smaller shards that are more performant than contemporary bigger shards while still maintaining the collective fault tolerance of the system. An elaborated theoretical analysis is presented which demonstrates the advantages of SecurShard in terms of fault tolerance and performance over traditional sharding systems.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".