Privacy-Preserving Federated Learning Framework for Distributed Chemical Process Optimization

This paper proposes a privacy-preserving federated learning framework for distributed chemical process optimization, addressing data confidentiality constraints in industrial plants. It enables collaborative model training across facilities by sharing only model parameters securely, maintaining data locality and industrial confidentiality.