Joint Energy and Latency Optimization in Federated Learning over Cell-Free Massive MIMO Networks

TL;DR

This paper introduces a power allocation scheme for federated learning over cell-free massive MIMO networks, optimizing energy and latency to improve training efficiency and accuracy.

Contribution

It proposes a novel uplink power allocation method considering inter-user effects, enhancing energy efficiency and reducing latency in federated learning over CFmMIMO.

Findings

Outperforms max-sum rate by up to 27% in test accuracy.

Improves max-min energy efficiency by up to 21%.

Reduces uplink energy and latency in FL training.

Abstract

Federated learning (FL) is a distributed learning paradigm wherein users exchange FL models with a server instead of raw datasets, thereby preserving data privacy and reducing communication overhead. However, the increased number of FL users may hinder completing large-scale FL over wireless networks due to high imposed latency. Cell-free massive multiple-input multiple-output~(CFmMIMO) is a promising architecture for implementing FL because it serves many users on the same time/frequency resources. While CFmMIMO enhances energy efficiency through spatial multiplexing and collaborative beamforming, it remains crucial to meticulously allocate uplink transmission powers to the FL users. In this paper, we propose an uplink power allocation scheme in FL over CFmMIMO by considering the effect of each user's power on the energy and latency of other users to jointly minimize the users' uplink energy and the latency of FL training. The proposed solution algorithm is based on the coordinate gradient descent method. Numerical results show that our proposed method outperforms the well-known max-sum rate by increasing up to~$27$\% and max-min energy efficiency of the Dinkelbach method by increasing up to~$21$\% in terms of test accuracy while having limited uplink energy and latency budget for FL over CFmMIMO.