无人机群在非独立同分布数据下用联邦学习协同训练，FedProx表现最稳定。
Distributed Learning for UAV Swarms
- 整合多种联邦学习算法，优化无人机群本地更新与全局聚合。
- 在非独立同分布数据下，所有算法性能下降，FedProx表现最优。
- 适合关注无人机群隐私协作、非独立同分布问题的研究者。
无人飞行器（UAV）群越来越多地部署于动态、数据丰富的环境，如环境监测和安防监控。这些场景要求高效处理数据的同时保障隐私与安全，联邦学习（FL）成为可行方案。FL使无人机群可协同训练全局模型而不共享原始数据，但因无人机采集的数据具有非独立同分布（non-IID）特性，带来挑战。本研究将先进的联邦学习方法应用于无人机群，评估了多种聚合方法（即FedAvg、FedProx、FedOpt和MOON）在不同数据集上的表现，包括用于基准测试的MNIST、用于自然物体分类的CIFAR10、用于环境监测的EuroSAT，以及用于安防的CelebA。所选算法覆盖了客户端更新与全局聚合的改进技术。结果表明，在独立同分布数据下各算法表现相近，但在非独立同分布条件下性能显著下降。其中，FedProx展现出最稳定的综合性能，凸显在非独立同分布环境中对本地更新进行正则化以缓解模型偏差的重要性。
原文摘要 · Abstract (English)
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.
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