Privacy-Preserving Federated Learning Framework for Distributed Chemical Process Optimization

A new paper on arXiv introduces a privacy-preserving federated learning framework for optimizing chemical processes across multiple plants. The framework, developed by Teetat Pipattaratonchai and Aueaphum Aueawatthanaphisut, allows each facility to train a local neural-network model using its own time-series sensor data. Only model parameters—not raw data—are transmitted to a central server via secure aggregation, enabling cross-plant knowledge sharing while maintaining strict data locality and industrial confidentiality.

Experimental evaluation using datasets from three independent chemical plants under heterogeneous conditions showed rapid convergence. The global mean squared error fell from approximately 2369 to below 50 within the first five communication rounds, stabilizing around 35 after 40 rounds. Compared to local-only training, the federated approach significantly improved prediction accuracy across all plants and achieved performance comparable to centralized training. The findings demonstrate that FL provides an effective, scalable solution for collaborative industrial analytics, enabling privacy-preserving predictive modeling and process optimization across distributed facilities.