Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO
This work addresses communication efficiency for federated learning in wireless networks, specifically for future scalable cell-free massive MIMO systems, representing an incremental improvement by adapting existing methods to a new domain.
The paper tackles the problem of communication overhead in federated edge learning by proposing an over-the-air federated learning implementation over cell-free massive MIMO, leveraging channel correlation and imperfect channel state information, with analytical and experimental convergence results confirming its benefits.
Cell-free massive MIMO is emerging as a promising technology for future wireless communication systems, which is expected to offer uniform coverage and high spectral efficiency compared to classical cellular systems. We study in this paper how cell-free massive MIMO can support federated edge learning. Taking advantage of the additive nature of the wireless multiple access channel, over-the-air computation is exploited, where the clients send their local updates simultaneously over the same communication resource. This approach, known as over-the-air federated learning (OTA-FL), is proven to alleviate the communication overhead of federated learning over wireless networks. Considering channel correlation and only imperfect channel state information available at the central server, we propose a practical implementation of OTA-FL over cell-free massive MIMO. The convergence of the proposed implementation is studied analytically and experimentally, confirming the benefits of cell-free massive MIMO for OTA-FL.