Malicious Model Detection for Federated Learning Empowered Energy Storage 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
Renewable energy plays an essential role in the energy sector and reducing carbon emissions. Energy storage is the key to releasing the full potential of renewable energy because it offers grid flexibility to ensure uninterrupted power to consumers. As a result, monitoring the operation of energy storage systems and ensuring it functions properly are foremost. Because of data scarcity and privacy concerns, multiple energy storage systems can collectively identify battery failures in a federated manner. However, such a federated learning paradigm introduces vulnerability to the system. Compromised energy storage systems can provide malicious models to jeopardize the convergence and accuracy of the global model. In order to address this problem, we propose an autoencoder-backed malicious model detection framework for federated learning empowered energy storage systems. We construct an autoencoder to calculate the reconstruction error for each updated model. By thresholding the reconstruction error with a predefined value, we are able to identify the malicious model parameters and stop them from model aggregation. Real-world experiments show that the proposed countermeasure efficaciously detects compromised model updates under strong attacks and outperforms state-of-art defense schemes.
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.
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