Energy-Efficient Federated Learning in Cooperative Communication within Factory Subnetworks

TL;DR

This paper proposes energy-efficient transmission protocols for federated learning in industrial subnetworks, optimizing relay-assisted communication to reduce energy consumption and latency.

Contribution

It introduces a novel relay-assisted transmission protocol with an optimization algorithm for energy efficiency in federated learning within factory subnetworks.

Findings

Significant reduction in outage probability.

At least twofold energy savings.

Faster convergence compared to single-hop transmission.

Abstract

This paper investigates energy-efficient transmission protocols in relay-assisted federated learning (FL) setup within industrial subnetworks, considering latency and power constraints. In the subnetworks, devices collaborate to train a global model by transmitting their local models at the edge-enabled primary access (pAP) directly or via secondary access points (sAPs), which act as relays to optimize the training latency. We begin by formulating the energy efficiency problem for our proposed transmission protocol. Given its non-convex nature, we decompose it to minimize computational and transmission energy separately. First, we introduce an algorithm that categorizes devices into single-hop and two-hop groups to decrease transmission energy and then selects associated sAPs. Subsequently, we optimize the transmit power, aiming to maximize energy efficiency. To that end, we propose a Sequential Parametric Convex Approximation (SPCA) method to configure system parameters jointly. Numerical results demonstrate a significant reduction in outage probability and at least a twofold savings in total energy consumption, together with faster convergence, compared with single-hop transmission.