Over-the-Air Federated Learning in User-Centric Cell-Free Networks
Bibliographic record
Abstract
This letter presents a new over-the-air federated learning (OTA-FL) system supported by a user-centric cell-free (UCCF) network. We propose a two-level hierarchical deep reinforcement learning (HDRL) framework that minimizes mean squared error (MSE) derived from convergence analysis by jointly optimizing AP-device association (ADA) and power control (PC). Specifically, a soft actor-critic (SAC) with customized data pre-processing is designed for addressing ADA, and a multi-actor-attention-critic (MAAC) with tailored preprocessing and policy networks is designed for handling PC with complex state-action space. Simulations show that our method improves MSE by at least 31% and achieves better OTA-FL convergence than its benchmarks.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.053 | 0.049 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".