Convolutional ensemble learning for edge intelligence

Deep Edge Intelligence targets the deployment of deep learning algorithms in the edge network. While training deep networks requires computational resources, edge devices frequently lack high computational power. Decentralized learning methods such as federated learning provide a solution for gathering limited information from edge devices and collectively improving prediction performance. However, a drawback of such methods is that they often require multiple rounds of network communication, which increases communication time and the risk of communication errors. Another drawback is that the same model architecture is often used on all edge devices, which makes it mandatory to work with devices above a level of computational capacity. This thesis proposes a hybrid learning approach that employs ensemble learning with a convolutional scheme for different edge model architectures, except for a selected fully connected layer of the same dimensionality. Initially, shallow neural networks are trained on edge devices until a certain level of performance is achieved. Next, the feature representations obtained by the shallow models are transferred to an ensemble model. Subsequently, the proposed convolutional ensemble model is trained to boost the prediction performance. This method facilitates the completion of the system training with a one-way data transfer between edge devices and the server. Variational auto-encoders are also utilized to generate feature vectors in case transferring the required representations from the edge devices fails. Extensive experiments demonstrate that the suggested method outperforms state-of-the-art techniques in terms of accuracy while requiring fewer communications and a lower amount of data in various training scenarios.