Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration across Distributed Data Sources

Abstract

Federated Learning (FL) is a decentralized machine learning approach that has gained attention for its potential to enable collaborative model training across clients while protecting data privacy, making it an attractive solution for the chemical industry. This work aims to provide the chemical engineering community with an accessible introduction to the discipline. Supported by a hands-on tutorial and a comprehensive collection of examples, it explores the application of FL in tasks such as manufacturing optimization, multimodal data integration, and drug discovery while addressing the unique challenges of protecting proprietary information and managing distributed data sets. The tutorial was built using key frameworks such as Flower and TensorFlow Federated and was designed to provide chemical engineers with the right tools to adopt FL for their specific needs. We compare the performance of FL against centralized learning across three different data sets relevant to chemical engineering applications, demonstrating that FL will often maintain or improve classification performance, particularly for complex and heterogeneous data. We conclude with an outlook on the open challenges in federated learning to be tackled and current approaches designed to remediate and improve this framework.