Signal Detection in Non-Cooperative Communications Using Federated Deep Learning
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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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".