Federated Learning-Enabled Smart Jammer Detection in Terrestrial and Non-Terrestrial Heterogeneous Joint Sensing and Communication Networks
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
In this letter, we propose a novel federated learning (FL) framework for detecting smart jamming in heterogeneous joint sensing and communication within terrestrial and non-terrestrial (HJSAC-TNT) networks. Our approach addresses the threat that signal-replicating smart jammers pose to unmanned aerial vehicle (UAV) operations by integrating a specially designed filtering technique, called a dynamic adaptive spectro-temporal resilience filter (DASTRF), into a local variational autoencoder (VAE) that has been enhanced with vision transformer (ViT) and long short-term memory (LSTM) units, called an FL-based ViT-LSTM-VAE. This setup effectively distinguishes between authentic signals and jamming interference by applying the DASTRF to the time-frequency distribution (TFD). It extracts discriminating features from unknown jamming without prior knowledge and refines waveform discrimination. Our FL framework significantly enhances the tradeoff between sensing and communication, thereby improving detection accuracy and jamming resistance with moderate time and resource complexity. This advancement ensures more reliable communications and secure target detection in complex network scenarios.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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