Study on the Selection Method of Federated Learning Clients for Smart Manufacturing

Artificial intelligence technology in the context of smart manufacturing uses manufacturing data to enable the automatic detection, classification, and identification of products in the production process, reducing production costs and human consumption, thereby improving production efficiency and product quality. Federated learning enables the distributed implementation of AI technologies, keeping data local to avoid privacy leaks. However, data heterogeneity factors have an impact on federated learning in a manufacturing context, and this paper proposes a customer degree selection method based on model parameter variation. The method relies on transmitting the local model changes in the participants to reflect the data characteristics, calculates the model similarity of the participants using graph theory and similarity, and uses the Top-K mechanism to filter the original participant set through the similarity scores of graph nodes to reduce the influence of heterogeneity factors in the participant set and maximize the training effect and accuracy of federated learning. The effectiveness of this method was verified by using the Dirichlet distribution to perform non-IID data partitioning on the power system attack dataset and the hard disk fault detection dataset.