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

2024-11-23

arXiv.org

AI Summary

To address cross-enterprise data silos and proprietary information protection challenges in chemical engineering, this work pioneers the systematic integration of federated learning (FL) into the domain, establishing a privacy-preserving, distributed collaborative modeling framework. Methodologically, we build upon Flower and TensorFlow Federated, incorporating heterogeneous data scheduling, robust model aggregation, and cross-domain evaluation to support representative tasks—including process optimization, multimodal data fusion, and drug discovery. We further propose the first FL-oriented pedagogical paradigm and practical guidelines tailored to chemical engineering, eliminating reliance on centralized data collection. Experiments on three real-world chemical engineering datasets demonstrate that, under strict data locality, federated models achieve classification performance comparable to or exceeding centralized training—particularly excelling in highly heterogeneous settings—thus effectively reconciling data sovereignty with modeling efficacy.

Technology Category

Machine Learning: Distributed Machine Learning & Federated LearningConstraint Satisfaction and Optimization: Distributed CSP/OptimizationNatural Language Processing: Learning & Optimization for NLP

Application Category

User Modeling, Personalization and Recommendation: Federated recommendation systems and personalizationSystems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processingGraph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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 datasets. The tutorial was built using key frameworks such as $ exttt{Flower}$ and $ exttt{TensorFlow Federated}$ and was designed to provide chemical engineers with the right tools to adopt FL in their specific needs. We compare the performance of FL against centralized learning across three different datasets 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.

Problem

• Privacy-preserving collaboration in chemical engineering
• Application of Federated Learning in distributed data
• Performance comparison of FL vs centralized learning

Innovation

• Decentralized machine learning approach
• Privacy-preserving collaborative model training
• Integration of Flower and TensorFlow Federated