A rapid Multi-source information integration method based on improved federated learning for a “dual carbon” smart monitoring center in a Dual-carbon context

To achieve the rapid unsupervised learning of multi-source information, this paper studies a multi-source information integration method for the “dual carbon” smart monitoring center based on the improved federated learning. To solve the problem of rapid integration information from many sources in the “dual carbon” smart monitoring center, a multimodal federated learning framework is built on the basis of the traditional federated learning. The generator and discriminator of the conditional generative adversarial network model are used to distinguish between the generated pseudo-samples and normal samples, and the multi-source information is obtained unsupervisedly. Based on the global distribution, the fast integration is achieved by using the passive distillation method of federated data. At the same time, the stochastic gradient descent is used to enhance the learning rate, improve the learning ability of the model, and promote the unsupervised fast fusion. The experiment shows that this method can effectively integrate the multi-source information, display the spatial status of carbon emissions and enterprise energy production data. The integrated information has high completeness and entropy value, and is accurate and applicable in the multi-source information integration of the “dual carbon” smart monitoring center.