Deploying Over-the-Air Federated Learning in Real-World Multi-Antenna Systems

Over-the-air federated learning (OTA-FL) harnesses the superposition property of wireless channels for efficient model aggregation, offering significant spectrum savings. However, practical deployment is challenged by analog distortions, synchronization errors, and dynamic gradient scaling across neural network layers. This paper presents a robust OTA-FL framework that systematically addresses these limitations. We introduce a gradient-aware power scaling technique that adjusts per-layer transmit power to mitigate the impact of uneven gradient magnitudes, coupled with channel pre-equalization for stable convergence. We implement OTA-FL on a real-world testbed with a multi-antenna gNB and develop a deep learning based multi-antenna combiner for scalable aggregation in dense networks. To enable reproducible and site-aware evaluation, we propose a digital twin framework that integrates ray-traced urban channels into a hardware-in-the-loop testbed via real-time channel emulation. Experimental results on a wireless positioning task demonstrate that our OTA-FL system achieves model accuracy comparable to digital FL while providing a 140× reduction in spectrum usage for 20 participants. This work closes the gap between OTA-FL theory and real-world deployment, highlighting practical solutions for scalable and spectrum-efficient FL.