Securing Next-Generation Networks against Eavesdroppers: FL-Enabled DRL Approach
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
Anticipated advancements in 5G wireless networks and beyond would necessitate an increased emphasis on security measures to accommodate the projected rise in demand for connections and services. Therefore, this paper aims to investigate the physical layer security (PLS) to evaluate the privacy of authorized users in multi-cellular networks, which represent a fundamental architecture in next-generation networks. Each cell is assumed to include a base station (BS) that serves multiple users. This scenario also takes into account the presence of several eavesdroppers. Every BS functions as a reinforcement learning (RL) agent that must undergo training in order to optimize security. To enhance the safety and speed of training, a federated learning (FL) technique is utilized. In this approach, a central unit regularly receives the neural network (NN) weights from the agents, updates them, and then transfers the result back to the agents to update their model. We examine and compare two deep RL methodologies, specifically deep Q-network, and Reinforce deep policy gradient. The findings of our research demonstrate the influence of the number of eavesdroppers on security, as well as the impact of the number of cells and the aggregation frequency of neural network parameters.
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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| 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