Distributed Learning for UAV Swarms

TL;DR

This paper explores federated learning techniques for UAV swarms, analyzing how different aggregation methods perform under non-IID data conditions across various datasets, highlighting FedProx's stability.

Contribution

It integrates advanced federated learning methods into UAV swarm applications and evaluates their effectiveness in non-IID data environments.

Findings

All algorithms perform similarly on IID data.

Performance drops significantly under non-IID conditions.

FedProx shows the most stable performance in non-IID environments.

Abstract

Unmanned Aerial Vehicle (UAV) swarms are increasingly deployed in dynamic, data-rich environments for applications such as environmental monitoring and surveillance. These scenarios demand efficient data processing while maintaining privacy and security, making Federated Learning (FL) a promising solution. FL allows UAVs to collaboratively train global models without sharing raw data, but challenges arise due to the non-Independent and Identically Distributed (non-IID) nature of the data collected by UAVs. In this study, we show an integration of the state-of-the-art FL methods to UAV Swarm application and invetigate the performance of multiple aggregation methods (namely FedAvg, FedProx, FedOpt, and MOON) with a particular focus on tackling non-IID on a variety of datasets, specifically MNIST for baseline performance, CIFAR10 for natural object classification, EuroSAT for environment monitoring, and CelebA for surveillance. These algorithms were selected to cover improved techniques on both client-side updates and global aggregation. Results show that while all algorithms perform comparably on IID data, their performance deteriorates significantly under non-IID conditions. FedProx demonstrated the most stable overall performance, emphasising the importance of regularising local updates in non-IID environments to mitigate drastic deviations in local models.