Cloud-RAN Over-the-Air Federated Learning

To address limited server coverage in traditional over-the-air federated learning (OA-FL), we introduce the framework of multiple input multiple output (MIMO) cloud radio access network (Cloud-RAN) OA-FL (MIMOCROF). This framework involves a two-step model aggregation method in each training round. Firstly, each base station (BS) aggregates the local updates from its served devices, resulting in an edge update. Secondly, the cloud server (CS) aggregates the edge updates from the BSs. By modeling the second step as a lossy distributed source coding (L-DSC) process, we analyze the performance of MIMOCROF from the perspective of rate-distortion theory, resulting in a unified communication-learning design approach. In the proposed design, we jointly optimize rate resources and beamforming vectors, thereby leveraging the correlation inherent in FL to achieve performance gains. Numerical results show that, by solving the optimization problem, MIMOCROF performs comparably to the error-free bound and significantly outperforms other benchmark schemes.