The IoT Service Agent Model based on Federated Learning to Improve Service Quality

The combination of learning analysis technology and terminal equipment can provide a rapidly imitating learning method between devices, and cognitive intelligence affects the learning effect. To enhance automated learning services, we can use federated learning methods to enable the training of different devices and enhance each other through devices. Using the training database to improve the quality of automated learning services. In this study, a novel agent-assisted active detection and data collection framework is designed. Monitoring agents can learn from each other to establish intelligent models, and through mutual communication between devices. Can check if established data can be applied to machine data model to get data. It can be used for intelligent manufacturing in the future. The agent may learn methods of learning and managing between devices having different properties. Obtaining experimental simulation and control data, and using machine learning to analyze growth progress and results allow for a deeper analysis of associated adjustments and anticipated changes.