EIDLS: An Edge-Intelligence-Based Distributed Learning System Over Internet of Things

With the rapid development of wireless sensor networks (WSNs) and the Internet of Things (IoT), increasing computing tasks are sinking to mobile edge networks, such as distributed learning systems. These systems benefit from the massive amounts of data and computing power on mobile devices and can learn qualified models on the premise of protecting user privacy. In fact, coordinating mobile devices to participate in computing is challenging. On the one hand, the heterogeneous performance of devices makes it difficult to guarantee computing efficiency. On the other hand, there are unreliable factors in the mobile network, which will destroy the stability of the distributed learning. Therefore, we design a three-layer framework called an edge-intelligence-based distributed learning system (EIDLS). Specifically, a novel multilayer perceptron-based device availability evaluation model is proposed to select devices with good performance. The evaluation model performs online learning and optimization according to the resources (CPU, battery, etc.) of devices. Meanwhile, we propose a dynamic trust evaluation algorithm to reduce the side effects of unreliable devices. The experimental results of some commonly used datasets validate that the proposed EIDLS dramatically minimizes the energy consumption and communication cost and improves the calculation accuracy and the stability of the system.