Visual object detection is a computer vision-based artificial intelligence (AI) technique which has many practical applications (e.g., fire hazard monitoring). However, due to privacy concerns and the high cost of transmitting video data, it is highly challenging to build object detection models on centrally stored large training datasets following the current approach. Federated learning (FL) is a promising approach to resolve this challenge. Nevertheless, there currently lacks an easy to use tool to enable computer vision application developers who are not experts in federated learning to conveniently leverage this technology and apply it in their systems. In this paper, we report FedVision - a machine learning engineering platform to support the development of federated learning powered computer vision applications. The platform has been deployed through a collaboration between WeBank and Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. Over four months of usage, it has achieved significant efficiency improvement and cost reduction while removing the need to transmit sensitive data for three major corporate customers. To the best of our knowledge, this is the first real application of FL in computer vision-based tasks.

RNA-binding proteins (RBPs) play a crucial role in the post-transcriptional regulation of RNAs. Identification of RBP binding sites is a key step to understand the biological mechanism of post-transcriptional regulation. Although many computational methods have been developed for predicting RNA-protein binding sites, few study considers the k-mer embedding representation of RNA primary sequence and secondary structure specificities. In this paper, we develop a general deep learning framework, named deepRKE, to predict RNA-protein binding sites. deepRKE takes an unsupervised shallow two-layer neural network to automatically learn the distributed representation of k-mers by taking their neighbor context into account. Compared to conventional k-mers approach, distributed representations effectively detect the latent relationship and similarity between k-mers. The distributed representations of the sequences and secondary structures are fed into CNN convolutional neural network (CNN) and a bidirectional long short term memory network (BLSTM) to discriminate the RBP binding sites from unbound sites. We comprehensively evaluate deepRKE on two large-scale RBP binding sites datasets, and the experimental results show that deepRKE achieves better performance than five competitive methods.