Over-the-Air Federated Learning in MIMO Cloud Radio Access Networks

Abstract

To address the limited server coverage of traditional over-the-air federated learning (OA-FL), we propose a new OA-FL framework for MIMO-based cloud radio access network (Cloud-RAN), called MIMO Cloud-RAN OA-FL (MIMOCROF). The proposed MIMOCROF consists of three stages in each training round. The first stage of edge aggregation allows each access point (AP) to collect local updates from edge devices and construct an edge update using MIMO multiple access. In the second stage of global aggregation, the cloud server (CS) aggregates edge updates received from the APs to form a global update through a fronthaul network. In the third stage of model updating and broadcasting, the CS sends the updated global model parameters to the APs, and the latter then broadcast the parameters to their served devices. To effectively exploit inter-AP correlation, we model the global aggregation stage as a lossy distributed source coding (L-DSC) problem. Based on the rate-distortion theory, we further analyze the performance of the MIMOCROF framework. We formulate a communication-learning optimization problem to improve the system performance by considering the inter-AP correlation. To solve this problem, we develop an algorithm by using alternating optimization (AO) and majorization-minimization (MM). Furthermore, we propose a practical L-DSC that exploits inter-AP correlation. Numerical results show that the proposed practical L-DSC effectively utilizes inter-AP correlation and is superior to other baseline schemes in performance.