Federated Online Learning for adaptive load forecasting across decentralized nodes
Bibliographic record
Abstract
Load forecasting is vital for a country’s power industry and economy, playing a crucial role in infrastructure planning, grid operation, and resource allocation. In recent years, Machine Learning (ML) techniques have been dominating load forecasting; however, traditional ML requires transferring data from different sensors, such as smart meters, to a central location for model training, and then the static models are used for forecasting. The main drawbacks are twofold: (1) data sharing and transfer results in privacy and security risks, and (2) static models miss opportunities to learn from new data. Federated learning has been proposed to address the first drawback while online learning tackles the second one. However, integrating the two remains challenging as it requires reconciling the distributed yet static paradigm of FL with the centralized yet dynamic sequential updating characteristics of online learning. Consequently, this paper proposes Federated Online Learning (FOL) which reconciles federated and online learning to provide adaptive distributed load forecasting. The local nodes employ a modified online learning technique based on a Long Short-Term Memory (LSTM) to learn from streaming data, while the federated learning process, including aggregation, is designed to accommodate sequential learning. Moreover, FOL includes communication control parameters to manage the exchange of information, synchronization, and network traffic. Experiments demonstrate that the proposed FOL outperforms traditional offline models and achieves similar results to online models while providing the benefits of adaptive distributed learning without local data sharing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".