Multi-Antenna Transceiver Design for Federated Learning in Cell-Free Massive MIMO System

Federated learning (FL) has garnered significant attention as an emerging distributed learning paradigm due to its inherent privacy protection features. Cell-free massive MIMO (CF-mMIMO) is a key enabler for future wireless communication systems, offering high reliability, energy efficiency, low latency, and seamless coverage to meet the demands of FL. This paper investigates an FL realization in a CF-mMIMO system with multi-antenna access points and user equipments, employing multi-datastream transmission to leverage the advantages of multi-antenna transmission. The results of FL convergence analysis under two typical cooperative reception scenarios in the CF-mMIMO system—fully-centralized processing (FCP) and large-scale fading decoding (LSFD)—are presented without predefining the transmission and reception strategies. Based on the analysis, the optimization of FL convergence performance was carried out. Through decoupling, the FL convergence performance optimization problem is ultimately transformed into a series of weighted sum-MSE minimization problems. The alternative optimization approach is employed to design iterative optimization algorithms for both cooperative reception scenarios, thereby achieving optimal convergence performance. Simulation experiments validate the effectiveness of the convergence analysis and demonstrate the advantages of multi-antenna multi-datastream transmission. Moreover, the proposed alternating iterative algorithms improve the convergence performance of FL under most experimental setups.