A Federated Meta Learning-Based Secure Data Consolidation Scheme for Industrial AIoT Leveraging Drone
Bibliographic record
Abstract
Amidst the technological revolution, the convergence of Industrial Artificial Intelligence of Things (Industrial AIoT) signifies a profound transformation in industrial operations. Nonetheless, persistent concerns revolve around data privacy, security, and connectivity challenges. Drones emerge as pivotal aids for Industrial AIoTs, particularly in areas with limited connectivity. While Federated Learning (FL) and Meta-Learning (ML) address data privacy and adaptability, challenges like data heterogeneity, scarcity, model positioning, unauthorized data tampering, and cyber threats endure. To tackle these issues, this paper presents a Federated Meta-Learning (FML)-based secure data consolidation scheme, utilizing drones for data consolidation, especially in remote, poorly connected regions, followed by secure blockchain storage. It incorporates an Information Gain Ratio (IGR)-based feature selection method to manage data diversity, a two-phase authentication system merging XOR filtering and Chronological Nonce Authentication for entity validation, and secure model consolidation using Hampel filters and performance checks to validate model updates. A real-world proof of concept demonstrates superior performance compared to state-of-the-art literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.012 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".