A Blockchained Federated Learning Framework for Cognitive Computing in Industry 4.0 Networks
Bibliographic record
Abstract
Cognitive computing, a revolutionary AI concept emulating human brain's reasoning process, is progressively flourishing in the Industry 4.0 automation. With the advancement of various AI and machine learning technologies the evolution toward improved decision making as well as data-driven intelligent manufacturing has already been evident. However, several emerging issues, including the poisoning attacks, performance, and inadequate data resources, etc., have to be resolved. Recent research works studied the problem lightly, which often leads to unreliable performance, inefficiency, and privacy leakage. In this article, we developed a decentralized paradigm for big data-driven cognitive computing (D2C), using federated learning and blockchain jointly. Federated learning can solve the problem of “data island” with privacy protection and efficient processing while blockchain provides incentive mechanism, fully decentralized fashion, and robust against poisoning attacks. Using blockchain-enabled federated learning help quick convergence with advanced verifications and member selections. Extensive evaluation and assessment findings demonstrate D2C's effectiveness relative to existing leading designs and models.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".