A Cross-Domain Intelligent Wireless Sensing Algorithm Based on Federated Learning and Blockchain
Bibliographic record
Abstract
Wireless sensing technology, analyzing signal propagation to sense environments, has advanced in smart homes, health monitoring and security. However, massive data and dynamic communication environments expose limitations in traditional methods: weak security, poor feature extraction, and high environmental dependency. To address these challenges in complex cross-domain scenarios, this paper proposes a collaborative secure framework FL-BLC, which integrates federated learning (FL) and blockchain (BLC), and designs the DB-SE-Yolov8 cross-domain intelligent sensing algorithm. The FL-BLC framework ensures tamper-proof transmission and data privacy by encrypting and hash-verifying locally trained model parameters before batch-writing them to the blockchain. The DB-SE-Yolov8 algorithm employs a dual-branch(DB) design: the upper branch employs average adaptive pooling for global features, while the lower branch employs the Squeeze-and-Excitation (SE) attention mechanism for attention features. A gating mechanism dynamically fuses multi-scale features, reducing complexity and enhancing accuracy across diverse scenarios. Compared with Dual-Attention CSI Network, Environment Independent and Joint Adversarial Domain Adaptation algorithms, DB-SE-Yolov8 significantly improves in-domain and cross-domain sensing performance. For cross-location and cross-orientation scenarios, the sensing accuracy is improved by 7.16% and 8.84%, while sensing efficiency is improved by 10.85% and 11.67%, respectively.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.013 | 0.023 |
| Research integrity | 0.001 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".