Distributed Learning Mechanisms for Anomaly Detection in Privacy-Aware Energy Grid Management Systems
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
Smart grids have become an emerging topic due to net-zero emissions and the rapid development of artificial intelligence (AI) technology focused on achieving targeted energy distribution and maintaining operating reserves. In order to prevent cyber-physical attacks, issues related to the security and privacy of grid systems are receiving much attention from researchers. In this paper, privacy-aware energy grid management systems with anomaly detection networks and distributed learning mechanisms are proposed. The anomaly detection network consists of a server and a client learning network, which collaboratively learn patterns without sharing data, and periodically train and exchange knowledge. We also develop learning mechanisms with federated, distributed, and split learning to improve privacy and use Q-learning for decision-making to facilitate interpretability. To demonstrate the effectiveness and robustness of the proposed schemes, extensive simulations are conducted in different energy grid environments with different target distributions, ORRs, and attack scenarios. The experimental results show that the proposed schemes not only improve management performance but also enhance privacy and security levels. We also compare the management performance and privacy level of the different learning machines and provide usage recommendations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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