Deep Federated Representations for Distributed and Secure Spectrum Sensing in Large-Scale CRNs
Bibliographic record
Abstract
Spectrum sensing in large-scale cognitive radio networks (CRNs) presents significant challenges, as it typically necessitates numerous static secondary users (SUs) to determine the spectrum state. Current cooperative spectrum sensing (CSS) methods require SUs to transmit their private sensing data to a central unit. This centralized approach not only raises security concerns but also leads to considerable communication overhead. To address these issues, this paper introduces FeRAP, a novel CSS framework based on unsupervised federated representation learning. We leverage the mobility of multiple SUs to collect spectrum sensing data, allowing them to collaboratively yet distributively train a learning model to determine the spectrum state. The FeRAP framework employs a novel deep federated$\beta$variational autoencoder ($\beta$-VAE) for distributed representation learning, which identifies independent latent variables and learns disentangled representations of the sensing data in a lowerdimensional space. Furthermore, Affinity Propagation (AP) is then trained locally on the learned representations at each cooperating SU to securely and autonomously infer the spectrum state. FeRAP is a fully data-driven solution, requiring no modelbased assumptions or prior knowledge of channel or signal characteristics for training. Numerical results demonstrate that FeRAP's CSS performance is on par with supervised deep learning-based CSS techniques. Extensive simulations conducted under various network settings and propagation environments confirm the effectiveness and scalability of FeRAP.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".