Federated Learning With Potential Partnership Identification for Accurate Prediction in Flexible Manufacturing System

In recent years, intelligent manufacturing has integrated industrial data and artificial intelligence technology, which has been a widely concerned development direction in the manufacturing industry. Industrial data integrity is the key factor for the successful implementation of intelligent manufacturing. However, in flexible manufacturing systems with multivariety and small-batch, it is hard to collect production data from all working conditions. Actually, for the purpose of status monitoring, data acquisition and annotation on complex mechanical components is also time-consuming and labor-intensive, which requires the assistance of professional domain knowledge. Faced with the challenge of incomplete data quantity and quality, federated learning is a promising paradigm of collaborative modeling, which ensures data privacy and fully utilizes distributed data information from different industrial users. However, due to the heterogeneity of data among industrial users, cooperation benefits cannot satisfy all industrial users. In this article, a novel federated learning cooperation framework is proposed to guide participants to choose the appropriate coalition and improve the benefits of participants. In this framework, the self-organizing incremental neural network is employed to generate prototypes that can effectively capture the distributional characteristics of raw data, obviating the necessity for industrial users to provide their raw data and labels. It offers recommendations for industrial users to foster collaboration by assessing the similarity among these prototypes. The collaborative tool wear prediction experiments demonstrate the effectiveness of the framework on industrial data.