Underwater Acoustic Spectrum Sensing Algorithm Based on Personalized Federated Learning and RepViG
Bibliographic record
Abstract
The Ocean Internet of Things (OIoT) has promoted the development of the ocean devices, which generate a large amount of underwater acoustic data. The transmission of underwater acoustic data requires a large amount of spectrum resources. Aiming at the problems of improving the utilization rate and security of spectrum resources, an underwater acoustic spectrum sensing algorithm based on personalized federated learning (PFL) and RepViG is proposed. A security protection framework is established for underwater acoustic data based on PFL. The common features of each underwater acoustic data are extracted through the meta-model. Each client only needs to fine-tune the parameters of the meta-model based on the local underwater acoustic data to achieve a personalized model. Based on the improved RepViT and the improved ViG, a dual-branch sensing model of RepViG is designed. In the improved RepViT, we employ the Haar wavelet downsampling (HWD) module to retain the low-frequency and high-frequency detail features of the underwater acoustic signal through multi-resolution. And we employ Interactive Convolution Block (ICB) to capture the relationship between local features and global features through multi-scale dynamic convolution kernels. In the improved ViG, we adopt the lightweight Star-Blcok module to reduce feature redundancy and enhance sensing efficiency. Compared with other algorithms, the simulation results show that the detection probability is increased by 7.6%, and the false alarm probability is reduced by 7.5%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".