# Edge Computing Based on Federated Learning for Machine Monitoring
###### Abstract
There are many types of machine control panels, and displayed data are mixed and diversified, which often causes difficulties in computer vision technology. [PERSON] et al. [18] developed a CNN model from the spectrograms of sound to establish machine monitoring technology. Two stethoscope sensors were used to capture the sound information about the environment of machines. The authors also applied Short-Time Fourier Transform to analysis spectrograms of sound to solve the problem of factory noise. Currently, deep learning models are usually deployed on cloud servers under the consideration of computing power. However, in this manner, the collected data need to be transmitted to the cloud to perform event detection tasks. It often leads to delays in the cloud computing model, communication costs, and privacy issues, and the task cannot be achieved in time. Machine-to-machine service platform had been proposed to enable the communication of end devices. Through the cooperation of end nodes, this mechanism can effectively reduce the network traffic and speed up overall performances [19]. Edge computing attempts to deploy the decision-making model to the terminal device. Due to the shortage of the end device in computing power and storage, only simple machine learning can be performed, and high accuracy may not be obtained in the end. In preliminary research, it is observed that the supervisors of the production machine control department generally believe that integrating digital platforms through the IoT system can help supervisors and operators to keep an eye on the production process, detect the occurrence of abnormal events, improve the efficiency of production machines, and optimize the production process. Most of these factories have more than ten kinds of production machines. The time of purchasing the machine equipment may vary a long period, and the brands and functions are also different. Therefore, there are considerable differences in machine control methods or status display panels. In order to complete the digital upgrade of different equipment, additional sensors and IoT technology are normally used to collect real-time production status data of machine equipment. The integrated system can analyze the data, predict the moment when the machine may fail, or detect problems in real time. This also includes preventing abnormal events such as machine equipment crash or even to the blockage of production materials, increase unnecessary costs, and affect the normal production capacity of the factory. Most existing approaches about machine monitoring focused on building a total solution of IoT to fit the needs of Industry 4.0. It would not be acceptable under cost considerations. To the best of our knowledge, many control panels of the machines for small- and medium-sized factories are standalone and do not have communication interfaces for transmitting data directly. It might violate the warranty or cause damage to the production machine if we install new communication interfaces on the control panel in a brute force manner. Therefore, we proposed vision-based IoT technology based on federated learning to solve the immediate machine monitoring problem. This paper focused on providing a general solution based on edge computing and cloud computing in IoT for machine monitoring in the manufacture of small- and medium-sized factory. For real-time consideration, edge computing and cloud computing models were seamlessly cooperated to perform information capture, event detection, and adaptive learning. The proposed IoT system processed regional low-level feature for detection and recognition in edge nodes. The cloud computing including fog computing was responsible for mid- and high-level features by federated learning network. The system fully utilized all resources in the integrated deep learning network to achieve high performance operations. The edge node was implemented by a simple camera embedded on a Terasic DE2-115 board to monitor machines and process data locally.