Federated Learning-Enabled Smart Jammer Detection in Terrestrial and Non-Terrestrial Heterogeneous Joint Sensing and Communication Networks
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".