Privacy-Preserving Federated-Learning-Based Net-Energy Forecasting
Bibliographic record
Abstract
Energy forecasting not only enables infrastructure planning and power dispatching but also reduces power outages and equipment failures. To preserve the customers’ privacy, federated learning (FL) can be used to build a global energy forecasting model where customers train local models on their data and only send the models’ parameters to the utility server. However, FL may still leak customers’ data privacy because revealing the model’s parameters enables adversaries to launch attacks such as model inversion and membership inference. Moreover, most existing works only focus on load forecasting while energy forecasting for net-metering systems has not been well investigated. In this paper, we address these limitations by proposing a privacy-preserving FL-based energy forecasting model for net-metering systems. First, based on the analysis of real power consumption and generation readings, we design a hybrid deep learning (DL)-based energy forecasting model to provide an accurate prediction. Then, we develop an efficient data aggregation scheme to preserve the customers’ privacy by encrypting their models’ parameters during the FL training. Our extensive experiments’ results demonstrate that our predictor is accurate and our data aggregation scheme provides privacy preservation with high communication efficiency.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".