A Federated Learning Model With Short Sequence To Point Mechanism For Smart Home Energy Disaggregation
Bibliographic record
Abstract
Residential households contribute significantly to the overall energy consumption in developed countries. To reduce their energy consumption, they need solutions that help them track the use of their appliances at home. Non-Intrusive Load Monitoring (NILM) with short Sequence-to-Point (Seq2Point) is a deep learning method used to track and recognize what appliances are used in the houses and their respective energy consumption. To apply NILM with short Seq2Point in the real world, a large amount of data from households must be collected and transferred to centralized servers for further analysis. Such a process does not always preserve consumers' security and privacy. To address this challenge, this paper proposes a combination of Federated Learning (FL) and NILM to make the entire system safe and trustworthy in order to avoid the risks of customers' privacy leakage. In FL, local models are developed on each consumer end and trained on local data instead of gathering data from all devices and sending it back to the central server. By using this method, data will be handled locally in a single residence while increasing the system's speed and security. The proposed model is evaluated using a real-life dataset (UK-Dale) of Appliance Level Electricity from the UK. The results show that our system provides better accuracy and preserves the privacy of consumers.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".