无人机集群的分布式学习
Distributed Learning for UAV Swarms
AI总结：
本文研究无人机集群中的联邦学习，针对非独立同分布数据挑战，在MNIST、CIFAR10、EuroSAT和CelebA上比较FedAvg、FedProx、FedOpt和MOON四种聚合方法，发现FedProx在非独立同分布条件下性能最稳定。
摘要：
无人机（UAV）集群正越来越多地部署在动态、数据丰富的环境中，用于环境监测和监视等应用。这些场景要求在保持隐私和安全的同时进行高效的数据处理，这使得联邦学习（FL）成为一种有前景的解决方案。FL允许无人机在不共享原始数据的情况下协作训练全局模型，但由于无人机采集的数据具有非独立同分布（non-IID）特性，带来了挑战。在本研究中，我们展示了将最先进的FL方法集成到无人机集群应用中，并研究了多种聚合方法（即FedAvg、FedProx、FedOpt和MOON）的性能，特别关注在各种数据集上应对非独立同分布问题，具体包括用于基线性能的MNIST、用于自然物体分类的CIFAR10、用于环境监测的EuroSAT以及用于监视的CelebA。选择这些算法是为了覆盖客户端更新和全局聚合两方面的改进技术。结果表明，虽然所有算法在独立同分布数据上表现相当，但在非独立同分布条件下其性能显著下降。FedProx表现出最稳定的整体性能，强调了在非独立同分布环境中正则化局部更新以缓解局部模型剧烈偏差的重要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
