Advancing Over-the-Air Federated Learning through Deep Reinforcement Learning in UAV-Assisted Networks with Movable Antennas

Computer and Knowledge Engineering

دوره 8، شماره 2 - شماره پیاپی 16، مرداد 2025، صفحه 1-10   

نوع مقاله: Internet of Thing (IoT)-Yaghmaee

شناسه دیجیتال (DOI): 10.22067/cke.2025.91290.1139

نویسندگان

Mohsen Ahmadzadeh¹؛ Saeid Pakravan²؛ Ghosheh Abed Hodtani^{* 1}

¹Department of Electric and Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.

²Department of Electric and Computer Engineering, Laval University, Quebec City, Canada,

چکیده

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).


 
 
 

کلیدواژه‌ها

Over-the-air federated learning؛ Deep reinforcement learning؛ Unmanned aerial vehicles؛ Movable Antenna

اصل مقاله

 

 

 

 

 

 

 

 

 

 

مراجع

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