Abstract
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.
Abstract (translated)
在本文中,考虑了无人机协助合作学习的问题。在这个场景中,一群智能无线设备通过无人机训练一个共享神经网络模型。利用每个设备传感器记录的环境样本,收集本地数据集进行训练。由于各种设备的数据量和传感器噪声水平不同,智能设备通过迭代训练本地数据集上的神经网络模型,并与无人机交换模型参数以聚合。为该系统,考虑了数据异质性、传感器噪声水平和通信错误等因素,并利用仅考虑数据特征或通信条件的基准线,计算出合作学习的收敛速率。提出了一种路径优化方法, aware of both the device data characteristics (i.e., local dataset size and noise level) and their wireless channel conditions, and significantly improves the convergence rate and final accuracy compared to baselines that only consider data characteristics or channel conditions。与最先进的基准线相比,该提出的方法在图像识别和语义分割基准数据集上的训练神经网络模型的最终准确性平均提高了3.85%和3.54%。此外,该框架在训练方面取得了显著加速,导致在这些任务中无人机 hover time、通信 overhead 和电池使用的平均节省分别为24%和87%。
URL
https://arxiv.org/abs/2303.02266
