Federated Learning and Blockchain-Based Collaborative Framework for Real-Time Wild Life Monitoring
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
Abstract Effective wildlife monitoring in hilly and rural areas can protect communities and diminish human-wildlife conflicts. A collaborative framework may overcome challenges like inadequate data integrity and security, declining detection accuracy over time, and delays in critical decision-making. The proposed study aims to develop a real-time wildlife monitoring framework using Federated Learning and blockchain to improve conservation strategies. Min-max normalization enhances training data and Elastic Weight Consolidation (EWC) for real-time adaptation. The improvised YOLOv8+EWC enables real-time classification and continual learning and prevents catastrophic forgetting. It also automates actions based on detection results using smart contracts and ensures secure, transparent data management with blockchain. Compared to existing classifiers such as Deep Neural Network, Dense-YOLO4, and WilDect: YOLO, YOLOv8+EWC performs exceptionally well across several metrics, accomplishing an accuracy of 98.91%. Thus, the proposed model enables reliable decision-making by providing accurate, real-time information about wildlife.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.008 |
| 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