用联邦学习实现化工数据隐私协作，提升模型性能。
Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sources
- 基于Flower和TensorFlow Federated框架，构建可落地的联邦学习流程。
- 在3个化工数据集上，联邦学习保持甚至超越集中式学习的分类效果。
- 适合需跨机构协作又保护机密数据的化工研发团队使用。
联邦学习（FL）是一种去中心化的机器学习方法，因其能在保护数据隐私的同时实现多方协同建模，成为化工行业的重要解决方案。本文面向化工工程领域提供易懂的入门指南，结合实践教程与完整案例，探讨了其在制造优化、多模态数据融合及药物发现中的应用，并应对专有信息保护与分布式数据管理等挑战。教程基于Flower和TensorFlow Federated等核心框架构建，旨在为化工工程师提供实用工具。我们在三个与化工相关的数据集上对比了联邦学习与集中式学习的表现，结果表明，联邦学习在复杂异构数据下通常能维持或提升分类性能。最后，论文展望了当前联邦学习面临的开放问题及改进策略。
原文摘要 · Abstract (English)
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 $\texttt{Flower}$ and $\texttt{TensorFlow Federated}$ 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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。