Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others. When participants are unmanned aerial vehicles (UAVs), UAV-enabled FL would experience heterogeneity due to the majorly skewed (non-independent and identically distributed -IID) collected data. In addition, UAVs may demonstrate unintentional misbehavior in which the latter may fail to send updates to the FL server due, for instance, to UAVs' disconnectivity from the FL system caused by high mobility, unavailability, or battery depletion. Such challenges may significantly affect the convergence of the FL model. A recent way to tackle these challenges is client selection, based on customized criteria that consider UAV computing power and energy consumption. However, most existing client selection schemes neglected the participants' reliability. Indeed, FL can be targeted by poisoning attacks, in which malicious UAVs upload poisonous local models to the FL server, by either providing targeted false predictions for specifically chosen inputs or by compromising the global model's accuracy through tampering with the local model. Hence, we propose in this paper a novel client selection scheme that enhances convergence by prioritizing fast UAVs with high-reliability scores, while eliminating malicious UAVs from training. Through experiments, we assess the effectiveness of our scheme in resisting different attack scenarios, in terms of convergence and achieved model accuracy. Finally, we demonstrate the performance superiority of the proposed approach compared to baseline methods.

翻译：联邦学习（FL）是一种去中心化机器学习（ML）技术，允许多个参与者在不共享私有本地数据集的情况下协同训练ML模型。当参与者为无人机（UAV）时，由于所采集数据的严重偏斜（非独立同分布，即Non-IID），无人机联邦学习将面临异构性问题。此外，无人机可能出现非恶意异常行为——例如因高机动性、不可用状态或电池耗尽导致与FL系统断连，从而无法向FL服务器发送更新。此类挑战可能显著影响FL模型的收敛性。现有应对方案之一是客户端选择，其基于考虑无人机计算能力与能耗的定制化标准。然而，多数现有客户端选择方案忽视了参与者的可靠性。实际上，FL可能遭受投毒攻击：恶意无人机通过上传含有毒性的本地模型至FL服务器，要么针对特定输入产生定向错误预测，要么篡改本地模型以破坏全局模型精度。为此，本文提出一种新型客户端选择方案，通过优先选择高可靠性评分的快速无人机并剔除训练中的恶意无人机来增强收敛性。通过实验，我们从收敛性与模型精度两方面评估了方案在抵御不同攻击场景下的有效性，最后证明了所提方法相较于基线方法的性能优越性。