Signal Detection in Non-Cooperative Communications Using Federated Deep Learning
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
This paper presents a novel framework for signal detection in non-cooperative communication environments using Federated Deep Learning (FDL).The increasing demand for robust signal detection in environments with multiple transmitters, such as cognitive radio networks, military communications, and unauthorized signal detection, necessitates advanced approaches that address privacy, adaptability, and computational efficiency.The proposed FDL framework combines the advantages of federated learning and deep learning to enhance the effectiveness of signal detection while maintaining data privacy.Federated learning allows distributed devices to collaboratively train a global model without sharing raw data.The decentralized approach is particularly suited for noncooperative environments, where channel dynamics are constantly changing, requiring adaptive and robust detection capabilities.By integrating deep learning models, the framework autonomously extracts complex features and learns from the vast, diverse datasets inherent to non-cooperative settings.The proposed FDL approach provides significant benefits, including enhanced adaptability, reduced network congestion, and improved robustness against interference.The paper also details the mathematical models and algorithms that underpin FDL, demonstrating its effectiveness in preserving data privacy.Results indicate that the FDL framework offers a scalable solution for real-time signal detection in dynamic environments, making it highly suitable for applications requiring secure and efficient communication.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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