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.

利用半监督联邦学习框架和模型参数混合策略来保护数据隐私实现无人机图像识别；根据不同相机模块、地域环境等数据的统计异质性问题，提出基于客户端频率的聚合规则来调整对应本地模型的权重。