Cell-Free Massive MIMO for Wireless Federated Learning

TL;DR

This paper introduces a cell-free massive MIMO scheme tailored for federated learning, optimizing training time through joint resource management, and demonstrating significant reductions in training duration compared to existing methods.

Contribution

It proposes a novel CFmMIMO scheme for FL, formulates a complex optimization problem, and develops an algorithm with proven convergence to enhance FL training efficiency.

Findings

Training time reduced by up to 55%

CFmMIMO outperforms other MIMO configurations in FL

Joint optimization effectively minimizes FL training duration

Abstract

This paper proposes a novel scheme for cell-free massive multiple-input multiple-output (CFmMIMO) networks to support any federated learning (FL) framework. This scheme allows each instead of all the iterations of the FL framework to happen in a large-scale coherence time to guarantee a stable operation of an FL process. To show how to optimize the FL performance using this proposed scheme, we consider an existing FL framework as an example and target FL training time minimization for this framework. An optimization problem is then formulated to jointly optimize the local accuracy, transmit power, data rate, and users' processing frequency. This mixed-timescale stochastic nonconvex problem captures the complex interactions among the training time, and transmission and computation of training updates of one FL process. By employing the online successive convex approximation approach, we develop a new algorithm to solve the formulated problem with proven convergence to the neighbourhood of its stationary points. Our numerical results confirm that the presented joint design reduces the training time by up to $55\%$ over baseline approaches. They also show that CFmMIMO here requires the lowest training time for FL processes compared with cell-free time-division multiple access massive MIMO and collocated massive MIMO.