Movable Antenna Design for UAV-Aided Federated Learning via Deep Reinforcement Learning
Bibliographic record
Abstract
This paper introduces an over-the-air federated learning (OTA-FL) framework that enhances learning efficiency by utilizing movable antennas (MAs) at the access point (AP), with unmanned aerial vehicles (UAVs) acting as federated learning (FL) clients to support Internet of Things (IoT) devices, particularly in remote or disaster-affected areas. We formulate a nonconvex optimization problem aimed at minimizing the Mean square error (MSE) through the joint optimization of antenna placement and beamforming vectors. To address the challenges posed by a dynamic environment, we recast the problem as a Markov decision process (MDP) and propose using the twin delayed deep deterministic policy gradient (TD3) algorithm. Extensive simulations show that the proposed TD3 approach outperforms systems with stationary antennas, including comparisons with fixed-position antennas (FPA) and alternative deep reinforcement learning (DRL) algorithms like soft actor-critic (SAC) and advantage actor-critic (A2C). The results highlight that combining MA arrays with TD3 improves OTA-FL performance and consistently yields higher average rewards, with MA systems proving more efficient than FPA systems.
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.
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 | 0.002 |
| 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) | 0.002 | 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".