IoT-Based Smart Farming Architecture Using Federated Learning: a Nitrous Oxide Emission Prediction Use Case
Bibliographic record
Abstract
Precision agriculture and smart farming can enable real-time decision-making to optimize resources and lower costs via data-driven model predictions. Adoption rates of smart farming systems are unfortunately low due to farmers’ privacy concerns and the high initial monetary costs of deploying such systems. High monetary costs can be lowered by replacing expensive sensing equipment with machine learning models. Cloud computing can be used to train models, but this suffers from poor privacy. Instead, fog and edge computing can train local models, but important geographical trends may be lost due to data segmentation. Federated learning can be used to address these challenges. A privacy-aware Internet of Things (IoT)-based smart farming architecture that uses federated learning was proposed. A prototype was deployed to gather sensor data from a local Canadian smart farm in Ottawa, Ontario. For various data-driven models, we perform nitrous oxide prediction experiments using centralized, local, federated, and distributed ensemble learning. We found that federated and ensemble learning can compete similarly well with centralized learning. Our results demonstrate that our methodology can potentially replace expensive nitrous oxide emission sensing equipment using inexpensive sensors combined with predictive analytics models.
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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 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".