# Enhanced Over-the-Air Federated Learning Using AI-based Fluid Antenna System

arXiv: https://arxiv.org/abs/2407.03481 · HTML: https://mlsanity.com/paper/2407.03481/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2024.

## Built on by (most cited first, 1 of 1)

- [Balancing Latency and Model Accuracy for Fluid Antenna-Assisted LM-Embedded MIMO Network](https://mlsanity.com/paper/2508.01689/) (2025)

## Abstract (arXiv)

This paper investigates an over-the-air federated learning (OTA-FL) system that employs fluid antennas (FAs) at an access point. The system enhances learning performance by leveraging the additional degrees of freedom provided by antenna mobility. We analyze the convergence of the OTA-FL system and derive the optimality gap to illustrate the influence of FAs on learning performance. With these results, we formulate a nonconvex optimization problem to minimize the optimality gap by jointly optimizing the positions of the FAs, the beamforming vector, and the transmit power allocation at each user. To address the dynamic environment, we cast this optimization problem as a Markov decision process and propose the recurrent deterministic policy gradient (RDPG) algorithm. Finally, extensive simulations show that the FA-assisted OTA-FL system outperforms systems with fixed-position antennas and that the RDPG algorithm surpasses the existing methods.