Application of federated learning in collaborative control of distributed mechanical production line

With the continuous development of the manufacturing industry in the direction of digitalization and intelligence, the distributed mechanical production line has encountered new technical problems in production efficiency, data security and multi-equipment collaborative control. The traditional centralized control mode has some limitations in both data privacy and multi-device collaboration. To address these problems, this study adopts federated learning to build a collaborative control model for distributed mechanical production lines and to develop the corresponding implementation scheme. According to the characteristics of production line data and the heterogeneity of equipment, the local model structure, global aggregation algorithm and communication mechanism are studied and designed, and these contents are integrated with the existing industrial control system. With the help of simulation experiments and the test of demonstration production line, the control accuracy, system response ability, communication efficiency and privacy protection effect are comprehensively evaluated. The experimental results show that this method clearly reduces the control error and beat fluctuation across various working conditions, and effectively reduces the communication burden while suppressing the risk of data leakage. This research provides a systematic verification reference for the practical application of federated learning in the field of industrial control, and has a good reference value for the intelligent cooperative operation of distributed mechanical production lines.