# Edge Computing Based on Federated Learning for Machine Monitoring
###### Abstract  
This paper focused on providing a general solution based on edge computing and cloud computing in IoT to machine monitoring in manufacturing 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 features for detection and recognition in edge nodes. Cloud-computing including fog computing was responsible for mid- and high-level features by using the 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 Terasic DE2-115 board to monitor machines and process data locally. Learning-based features were generated by cloud computing through the data sent from edge and the identification results could be obtained by combining mid- and high-level features with the nonlinear classifier. Therefore, each factory could monitor the real-time condition of machines without operators and keep its data privacy. Experimental results showed the efficiency of the proposed method when compared with other methods. computer vision; edge computing; federated learning; Internet of Things +
Footnote †: journal: Applied  
computing that integrates intelligence into edge devices in IoT. They are also often referred to as edge nodes because they can process and analyze data in real-time near the source of data collection. In edge computing, data do not need to be uploaded directly to the cloud or to a centralized data processing system [10]. [PERSON] et al. [11] provided an article to introduce the current manufacturing industries and computer numerical control tool for machine production in factories. They also provided a brief review of machine monitoring and the summary of investments for machine monitoring in different countries. It showed the importance of the research needs for machine monitoring. Quasi-arithmetic means were proposed by [PERSON] et al. for machine monitoring. They investigated several measuring tools based on Quasi-arithmetic means to measure the machine conditions [12]. However, the data of machine condition should be collected by using the standard interface of the machine. For some machines of different ages, it is almost impossible to build a standard interface under cost considerations. Various artificial intelligence algorithms are designed to imitate human thinking and simulate recognition systems. By using various mathematical models and sufficient training data, it presents its ability to learn, recall, and inductively perform deduction. In addition, it has good results in dealing with image recognition, classification, normalization, optimization, and other problems. Currently, computer vision technology is very useful in recognizing objects with camera sensors in IoT [13; 14; 15]. Convolutional Neural Network (CNN) is a feedforward neural network for which its artificial neurons can respond to units within a partial coverage area and produce better results in computer vision and image processing. The network architecture of CNN usually includes multiple layers such as the input layer, convolution layer, and pooling layer. Among them, multiple convolutional layers or pooling layers can be repeated to achieve the purpose of deep learning. Finally, the training of the fully connected layer is added to achieve the prediction result of the output layer. If a large amount of training data can be matched, there will usually be a better identification result. Deep learning makes computer vision more robust for solving real problems such that IoT will also be more intelligent [16; 17]. However, high accuracy in computer vision requires powerful computing power, which is not an easy task that edge nodes can afford.