Advancing Over-the-Air Federated Learning through Deep Reinforcement Learning in UAV-Assisted Networks with Movable Antennas
Bibliographic record
Abstract
This paper investigates the deployment of over-the-air federated learning (OTA-FL), leveraging the dynamic repositioning and line-of-sight communication capabilities of unmanned aerial vehicles (UAVs) and movable antennas to enhance network efficiency. A closed-form expression is derived to quantify the optimality gap between the actual federated learning (FL) model and its theoretical ideal, accounting for the capabilities of movable antennas to show the diverse relationship between Mean Square Error (MSE) and the optimality gap. Then An MSE minimization problem is then formulated, involving the joint optimization of moveable antenna position vectors, and the beamforming vector at the UAV. This complex non-convex problem is reformulated as a Markov Decision Process (MDP) and solved using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm within the deep reinforcement learning (DRL) framework. Numerical results demonstrate that the proposed algorithm outperforms benchmarks such as Advantage Actor-Critic(A2C) and Soft Actor-Critic (SAC).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".