  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.
