Energy-efficient Federated Learning for UAV Communications

TL;DR

This paper introduces an energy-efficient federated learning framework using UAVs, optimizing trajectory, participation, power, and data to reduce energy use, supported by theoretical analysis and iterative optimization algorithms.

Contribution

It presents a novel UAV-assisted FL framework with joint optimization of multiple parameters and a new ECO algorithm, advancing energy efficiency in UAV communications.

Findings

ECO algorithm reduces energy consumption compared to baselines.

Theoretical convergence analysis links user participation to FL performance.

Simulation confirms ECO's superior energy efficiency.

Abstract

In this paper, we propose an unmanned aerial vehicle (UAV)-assisted federated learning (FL) framework that jointly optimizes UAV trajectory, user participation, power allocation, and data volume control to minimize overall system energy consumption. We begin by deriving the convergence accuracy of the FL model under multiple local updates, enabling a theoretical understanding of how user participation and data volume affect FL learning performance. The resulting joint optimization problem is non-convex; to address this, we employ alternating optimization (AO) and successive convex approximation (SCA) techniques to convexify the non-convex constraints, leading to the design of an iterative energy consumption optimization (ECO) algorithm. Simulation results confirm that ECO consistently outperform existing baseline schemes.