Federated Learning-Based Jamming Detection for Distributed Tactical Wireless Networks
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Bibliographic record
Abstract
In this paper, we propose a federated learning (FL)-based JDWC algorithm for distributed tactical wireless networks (TWNs). Specifically, we consider a distributed TWN with multiple clusters under the presence of a mobile jammer, where various types of waveforms are used over the network. On local servers, we perform frequency domain analysis of the received waveforms to extract the unique features from the spectral correlation function (SCF) of each waveform and use these features for training local convolutional neural networks (CNNs) to detect the jammer attacks and classify waveforms. Moreover, considering a practical distributed TWN where each cluster head (CH) has a partial observation of the TWN with insufficient data samples, the proposed algorithm exploits the distributed learning feature of FL, i.e., global learning aggregation, to detect the existence of jammers and to distinguish the types of received waveforms over the entire TWN. We implement a rigorous TWN simulation using Matlab Toolboxes and our proposed algorithm using TensorFlow Federated (TFF). Numerical results show that the proposed algorithm outperforms the standalone local SCF-CNN algorithm. We further demonstrate that using the SCF feature provides more accuracy than using the In-phase/Quadrature (I/Q) features.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.000 | 0.003 |
| 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