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%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".