Blockchain-based federated learning for Industry 5.0 applications
Bibliographic record
Abstract
Industry 5.0 is the newly updated standard of Industry 4.0, the aim of which is to incorporate human intelligence with machine intelligence for increasing the production of the manufacturing industry. In order to achieve this objective, all components in the supply chain right from the supplier to production lines, and then to end users are transferred to Internet-of-Things (IoT). These IoT devices require to learn some machine learning (ML) model time to time from the data acquired by themselves owing to the charismatic ability of artificial intelligence (AI). Because of the inconvenience in centralized learning and data privacy, federated learning (FL) is considered as one of the promising decentralized learning techniques. Despite some controversy, this learning technique still suffers from many privacy and security issues. Meanwhile, blockchain is a well-known technique to handle the privacy and security issues of a network in a decentralized manner. To this end, in this chapter, we present two application-specific blockchain-enhanced FL architectures to satisfy the requirements of Industry 5.0. Then, we discuss some security attacks and the potential of the proposed two architectures to counter-measure these attacks. Through some preliminary experiment results, we justify the effectiveness of the proposed two architectures. Finally, we outline some Industry 5.0-specific open challenges in the context of FL and blockchain technologies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".