Architecture and Implementation of 3D Engine Based on WebGL

As the progress of 3D rendering technology and the changes of market demand, the 3D application has been widely used and reached as far as education, entertainment, medical treatment, city planning, military training and so on. Its trend is gradually changed from client to web, and so many people start to research the 3D graphics engine technology on the web. WebGL and HTML5 rise in recent years and WebGL solves two problems of interactive 3D application on the web perfectly. Firstly, it implements the interactive 3D web application by JavaScript without any browser plug-in components. Secondly, it makes graphics rendering using the underlying graphics hardware, which is united, standard and cross-platform OpenGL interface. However, it is very difficult for 3D application web programmer to understand multifarious details. Therefore, a 3D engine based on WebGL comes into being. The paper consults the existing 3D engine design idea, architecture and implementation experience, and designs a 3D graphics engine based on WebGL and Typescript.

Joint Participant Selection and Learning Optimization for Federated Learning of Multiple Models in Edge Cloud

To overcome the limitations of long latency and privacy concerns from cloud computing,edge computing along with distributed machine learning such as federated learning(FL),has gained much attention and popularity in academia and industry.Most existing work on FL over the edge mainly focuses on optimizing the training of one shared global model in edge systems.However,with the increasing applications of FL in edge systems,there could be multiple FL models from different applications concurrently being trained in the shared edge cloud.Such concurrent training of these FL models can lead to edge resource competition(for both computing and network resources),and further affect the FL training performance of each other.Therefore,in this paper,considering a multi-model FL scenario,we formulate a joint participant selection and learning optimization problem in a shared edge cloud.This joint optimization aims to determine FL participants and the learning schedule for each FL model such that the total training cost of all FL models in the edge cloud is minimized.We propose a multi-stage optimization framework by decoupling the original problem into two or three subproblems that can be solved respectively and iteratively.Extensive evaluation has been conducted with realworld FL datasets and models.The results have shown that our proposed algorithms can reduce the total cost efficiently compared with prior algorithms.