Privacy-Preserving Federated Learning Framework for Distributed Chemical Process Optimization

This paper proposes a privacy-preserving federated learning framework for distributed chemical process optimization, enabling collaborative model training across multiple geographically separated plants without sharing raw data. The framework significantly improves prediction accuracy across plants and offers a scalable solution for privacy-preserving industrial analytics.