The use of edge devices together with cloud provides a collaborative relationship between both classes of devices where one complements the shortcomings of the other. Resource-constraint edge devices can benefit from the abundant computing power provided by servers by offloading computationally intensive tasks to the server. Meanwhile, edge devices can leverage their close proximity to the data source to perform less computationally intensive tasks on the data. In this paper, we propose a collaborative edge-cloud paradigm called ECAvg in which edge devices pre-train local models on their respective datasets and transfer the models to the server for fine-tuning. The server averages the pre-trained weights into a global model, which is fine-tuned on the combined data from the various edge devices. The local (edge) models are then updated with the weights of the global (server) model. We implement a CIFAR-10 classification task using MobileNetV2, a CIFAR-100 classification task using ResNet50, and an MNIST classification using a neural network with a single hidden layer. We observed performance improvement in the CIFAR-10 and CIFAR-100 classification tasks using our approach, where performance improved on the server model with averaged weights and the edge models had a better performance after model update. On the MNIST classification, averaging weights resulted in a drop in performance on both the server and edge models due to negative transfer learning. From the experiment results, we conclude that our approach is successful when implemented on deep neural networks such as MobileNetV2 and ResNet50 instead of simple neural networks.

翻译：边缘设备与云端的结合为两类设备提供了互补关系，其中一方弥补另一方的不足。资源受限的边缘设备可通过将计算密集型任务卸载至服务器，从而受益于服务器提供的强大计算能力。与此同时，边缘设备可利用其与数据源的邻近性，对数据执行计算需求较低的任务。本文提出一种名为ECAvg的协同边缘-云范式：边缘设备在其各自数据集上预训练局部模型，并将模型传输至服务器进行微调。服务器将预训练权重平均化为全局模型，并基于各边缘设备合并后的数据对全局模型进行微调。随后，局部（边缘）模型使用全局（服务器）模型的权重进行更新。我们采用MobileNetV2实现CIFAR-10分类任务、ResNet50实现CIFAR-100分类任务，并使用含单隐藏层的神经网络实现MNIST分类任务。实验结果表明，该方法在CIFAR-10和CIFAR-100分类任务中性能有所提升：采用平均权重的服务器模型性能增强，边缘模型在模型更新后也表现出更优性能。而在MNIST分类任务中，由于负迁移学习的影响，平均权重导致服务器和边缘模型的性能均出现下降。基于实验结果，我们得出结论：该方法适用于MobileNetV2和ResNet50等深度神经网络，但不适用于简单神经网络。