Privacy-Prioritized Model Aggregation in ICPS: A Novel Approach to Federated Learning Aggregation With Lime and Blockchain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper contributes to the fields of Federated Learning (FL) and Industrial Cyber-Physical Systems (ICPS) privacy. It introduces a novel model aggregation technique aimed at prioritizing privacy protection for sensor data collected by Integrated Sensing Digital Devices (ISDD) during the aggregation process. By incorporating Lime, a local explanation technique, and Blockchain technology, the approach enhances both transparency and security in the global model update process. Furthermore, the implementation of transfer learning strengthens the adaptability of attack detection systems to evolving threats within the dynamic ICPS landscape. The paper also proposes a comprehensive privacy evaluation method, providing a systematic assessment of privacy measures within the FL context. Comparative evaluations against FedAVG underscore the superior adaptability, accuracy, and privacy enhancement capabilities of the proposed Lime AGG model, particularly in scenarios involving previously unseen attacks which is evaluated by CICIDS 2017 and 2018 datasets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it