Joint Communication and Computation for Federated Learning Over Cell-Free MIMO Network

This paper proposes a joint communication and computation scheme (JCCS) for cell-free multiple-input multiple-output (CF-MIMO) network to support federated learning (FL). The JCCS allows users to choose between a model update process or a data offloading process, where the offloaded data and the uploaded model gradient are respectively sent to the distributed processing units (DPUs) deployed on the access points (APs) for further model updates. Moreover, we define a performance metric called iteration error gap as the difference between model errors of adjacent iterations and decouple the total training time minimization problem into the error gap maximization problem within fixed time limit. Base on common machine learning (ML) assumptions, we derive a lower bound of iteration error gap, which is determined by the minimum batch size among all DPUs. An optimization problem aiming to maximize the minimum batch size is then formulated to jointly optimize the time division, power control, and user selection. By employing the block coordinate descent approach, we develop a new algorithm to solve the formulated non-convex mixed integer programming problem. Our simulation results verify the convergence of proposed algorithm and show that it reduces the relative error of a single FL iteration by more than 1.5 dB compared with other baseline schemes. Furthermore, the CF-MIMO network integrated with JCCS achieves a relative error reduction exceeding 1 dB per iteration when compared to collocated MIMO network. In addition, an FL example of handwritten digits classification shows that the JCCS indeed accelerates the convergence of FL model.