Enterprise Multi-Department Data Collaborative Analysis Method Based on Federated Learning and Edge Computing

With the increasing demand for industrial automation, data collaboration between various departments within enterprises has become increasingly important. However, data sharing between departments is often restricted due to privacy and security concerns. This paper proposes an innovative approach based on federated learning and edge computing, aiming to optimize data processing and analysis within enterprises. By training models locally on edge devices and synchronizing model parameters using a federated learning framework, this method ensures data privacy while achieving cross-departmental data collaboration and global optimization.