Security Impact of Federated and Transfer Learning on Network Management Systems with Fuzzy DEMATEL Approach
Bibliographic record
Abstract
Everyday using of the big data, machine learning algorithms, and related studies, ensuring data privacy and security have become a critical necessity. These features make them more vulnerable to cyber-attacks. The security of the stored data is also critical, and evaluating the processing of information in the autonomous network management of these systems. The criteria considers the account in the processing and security of data entering every field from the widespread industry examined. It is necessary to increase their awareness of negative and attack problems while these systems are working. Applications such as traditional machine learning and the use of cloud computing also involve risks regarding data security and personal data leakage. Cooperative learning pays due attention to the confidentiality of sensitive information by keeping the original training data hidden. By collecting, combining, and integrating heterogeneous data with collaborative learning together with a federated learning structure, data produced and stored. This study discusses the effect of federated and transfer learning on autonomous network management analyzes the security status parameters. The fuzzy DEMATEL method was preferred in exploring the parameters affecting the system state according to the degree of importance. Situational scenarios evaluated by considering the structure in which the features of cyber-physical systems examined together with federated learning. Data security factors discussed with the fuzzy DEMATEL
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".