In this paper, the problem of drone-assisted collaborative learning is considered. In this scenario, swarm of intelligent wireless devices train a shared neural network (NN) model with the help of a drone. Using its sensors, each device records samples from its environment to gather a local dataset for training. The training data is severely heterogeneous as various devices have different amount of data and sensor noise level. The intelligent devices iteratively train the NN on their local datasets and exchange the model parameters with the drone for aggregation. For this system, the convergence rate of collaborative learning is derived while considering data heterogeneity, sensor noise levels, and communication errors, then, the drone trajectory that maximizes the final accuracy of the trained NN is obtained. The proposed trajectory optimization approach is aware of both the devices data characteristics (i.e., local dataset size and noise level) and their wireless channel conditions, and significantly improves the convergence rate and final accuracy in comparison with baselines that only consider data characteristics or channel conditions. Compared to state-of-the-art baselines, the proposed approach achieves an average 3.85% and 3.54% improvement in the final accuracy of the trained NN on benchmark datasets for image recognition and semantic segmentation tasks, respectively. Moreover, the proposed framework achieves a significant speedup in training, leading to an average 24% and 87% saving in the drone hovering time, communication overhead, and battery usage, respectively for these tasks.

翻译：本文研究了无人机辅助的协同学习问题。在该场景中，智能无线设备集群借助无人机训练共享神经网络模型。每台设备利用其传感器从环境中采集样本，构建本地训练数据集。由于各设备数据量及传感器噪声水平存在差异，训练数据呈现严重异质性。智能设备在本地数据集上迭代训练神经网络，并将模型参数与无人机进行交换以实现聚合。针对该系统的协同学习收敛速率，本文在考虑数据异质性、传感器噪声水平及通信误差的基础上进行了推导，进而获得了能最大化训练后神经网络最终精度的无人机轨迹。所提出的轨迹优化方法同时感知设备数据特征（即本地数据集大小与噪声水平）及其无线信道条件，与仅考虑数据特征或信道条件的基线方法相比，显著提升了收敛速率与最终精度。与现有最先进基线相比，所提方法在图像识别和语义分割基准数据集上，使训练后神经网络的最终精度平均提升3.85%和3.54%。此外，该框架实现了训练过程的显著加速，在上述任务中分别实现无人机悬停时间、通信开销和电池使用量平均节省24%和87%。