Federated Learning-Based Jamming Detection for Distributed Tactical Wireless Networks
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".