Air access networks have been recognized as a significant driver of various Internet of Things (IoT) services and applications. In particular, the aerial computing network infrastructure centered on the Internet of Drones has set off a new revolution in automatic image recognition. This emerging technology relies on sharing ground truth labeled data between Unmanned Aerial Vehicle (UAV) swarms to train a high-quality automatic image recognition model. However, such an approach will bring data privacy and data availability challenges. To address these issues, we first present a Semi-supervised Federated Learning (SSFL) framework for privacy-preserving UAV image recognition. Specifically, we propose model parameters mixing strategy to improve the naive combination of FL and semi-supervised learning methods under two realistic scenarios (labels-at-client and labels-at-server), which is referred to as Federated Mixing (FedMix). Furthermore, there are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules in different environments, i.e., statistical heterogeneity. To alleviate the statistical heterogeneity problem, we propose an aggregation rule based on the frequency of the client's participation in training, namely the FedFreq aggregation rule, which can adjust the weight of the corresponding local model according to its frequency. Numerical results demonstrate that the performance of our proposed method is significantly better than those of the current baseline and is robust to different non-IID levels of client data.
翻译:航空访问网络被公认为是各种物联网(IoT)服务和应用的重要驱动力,特别是德龙州互联网上的航空计算网络基础设施在自动图像识别方面引发了一场新的革命。这种新兴技术依赖于在无人驾驶航空飞行器群群之间共享地面真相标签数据,以训练高质量的自动图像识别模型。然而,这种方法将带来数据隐私和数据提供挑战。为了解决这些问题,我们首先提出了一个半监督的联邦学习(SSFL)框架,用于隐私保护UAV图像识别。具体地说,我们提出了模型参数混合战略,以改善FL和半监督的学习方法在两种现实情景下天真的结合(标签-当客和标签-服务机群),这被称为FedMix(FedMix)。此外,在使用不同环境的不同摄像模块收集的本地数据的数量、特征和分布方面差异很大,例如,统计遗传特征和半监督的学习方法,我们提议,在统计的频率上,对客户进行更精确的比对数据进行更精确的频率调整。我们提议,在对当前数据进行更精确的频率上进行更精确的分类,我们建议,对数据进行更精确的频率进行更精确的计算。