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Deep Edge Intelligence targets the deployment of deep learning algorithms at the edge devices to make them contribute to the overall intelligence in the Internet of Things. While training deep networks requires computational resources, edge devices usually lack high computational power. Decentralized online learning methods provide a solution for gathering limited information from edge devices to collectively improve prediction performance. However, methods that require multiple rounds of data transfer between edge components for an acceptable accuracy increase not only the training time but also the risk of communication errors due to connectivity failures that result in packet delays and losses. Besides, every device at the edge may not experience the same training data due to limitations set by physical constraints such as the location of devices and environmental factors. To overcome these bottlenecks, this study proposes a convolutional ensemble learning approach, coined EdgeConvEns, that facilitates training heterogeneous weak models on edge and learning to ensemble them by fusing the limited knowledge obtained from edge where data on edge are heterogeneously distributed. In this setup, it is possible for an edge device to barely train a shallow network that fits its resources. Losses that occur while transferring their feature representations to the edge server are handled by variational autoencoders, which learn to fill in missing representations. The last stage is the proposed convolutional ensemble model at the edge server which uses all representations during training and inference to boost the prediction performance. The overall proposed approach can also be employed in setups where edge devices can finely tune pre-trained inferences. To accelerate the training on edge devices, a flexible chained-tiling method is also proposed and demonstrated on field programmable gate array (FPGA) devices with various computational capacities. Extensive experiments demonstrate that EdgeConvEns can outperform the state-of-the-art performance with fewer communications and less data in training scenarios selected from the literature.