For a blockchain, consensus is the foundation protocol that enables cryptocurrencies such as Bitcoin to maintain state. Additionally, to ensure safety and liveness for a publicly accessible and verifiable ledger, fault tolerance must be robust. However, there appears to be a degree of misunderstanding about how consensus is applied across blockchains. To assist researchers considering variations between them, this study presents a rational classification of consensus methods applied to current blockchains. The study provides a survey of 19 methods classified by the scarce resource they employ: clock-cycles, bits, tokens, votes, time, and biometrics. Blockchain implementations are split between consensus algorithms requiring proof of resource and those that use majority voting to update the ledger.

The ubiquity of GitHub for software developers to coordinate software development in a community platform has resulted in a rich source of public data. Blockchain teams put open-source code as a founding principle since the release of Bitcoin and nearly all blockchain-based projects have code visible on GitHub. Developer engagement is known to be important to the health and viability of open-source software, yet has varying definitions and no standard method of measuring what constitutes engagement. This work uses exploratory factor analysis to identify dimensions that represent engagement in a community of open-source developers. We find that a latent factor composed of pull-requests, commits, comments, and authors based on a monthly average of the previous three months is a representation of Developer Engagement. A secondary factor consists of stars, forks, and total authors. Cross validation of the dataset is carried out with good support for the model.

Federated learning enables industrial operators to train shared intrusion detection models without disclosing proprietary operational telemetry. However, existing defences operate strictly in update space, leaving aggregators blind to data poisoning; model updates derived from fabricated telemetry remain indistinguishable from honest contributions. We repurpose process invariants, physical relations that always hold in normal operation, such as the conservation of mass, from runtime detection heuristics into a verifiable admission requirement for federated updates, mined automatically from clean operational data. We evaluate this admission gate across two physical water testbeds (SWaT and WADI) and a distribution benchmark (BATADAL), testing seven aggregation rules against telemetry fabrication, exposure-only replay poisoning, and an invariant-aware adaptive adversary. Across three testbeds, the mined invariants reject none of 25 honest shards except every cross-channel fabrication on SWaT and BATADAL, including optimised perturbations that the similarity-based FoolsGold admits in full. On real telemetry, five mined invariants detect 12 of SWaT’s 35 attacks, while nine invariants detect 20. With nine invariants, the admission gate removes 78% of the targeted-attack recall lost to replay poisoning (95% CI 57–97%, $p<0.001$ over 20 seeds) across five standard aggregators and 82% under FedAvg. To reconcile physical admission control with federated data privacy, we show invariant compliance using zero-knowledge proofs (zk-SNARKs) to allow clients to prove batch adherence without revealing operational telemetry.

A few points before this can be considered a research article: -further review of consensus methods are necessary. There are many more methods than the ones mentioned, and the detail is important. For example, how many clock cycles are required for...